Technical terms are specialized words or phrases that have specific meanings within a particular technical field, industry, or domain of knowledge. … more Technical terms are specialized words or phrases that have specific meanings within a particular technical field, industry, or domain of knowledge.

Natural language processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and human language, enabling machines to understand, interpret, and generate human language. … more Natural language processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and human language, enabling machines to understand, interpret, and generate human language.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks that typically require human intelligence. … more Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks that typically require human intelligence.

"Hyperparameter" refers to a configurable value that governs the operation of a machine-learning model, such as parameters that control the training process (e.g., learning rate, batch size, and number of epochs) and/or parameters that control inference-time generation (e.g., temperature, top-p, and maximum token limits). Hyperparameters are selected or tuned externally to the model and are not learned as part of the model weights. … more "Hyperparameter" refers to a configurable value that governs the operation of a machine-learning model, such as parameters that control the training process (e.g., learning rate, batch size, and number of epochs) and/or parameters that control inference-time generation (e.g., temperature, top-p, and maximum token limits). Hyperparameters are selected or tuned externally to the model and are not learned as part of the model weights.

“Training record” or “Training records” refers to a collection of two-part data structures used to train a machine learning model. Each record comprises a first component (such as an input, stimulus, or query) and a second component (such as a corresponding label, target output, response, feedback, or the like), which are associated for the purpose of supervised, semi-supervised learning, or related machine-learning processes. Training records may be used to teach or train a model to associate certain types of inputs with corresponding outputs by adjusting internal parameters to minimize error between predicted and actual outputs. Examples include, but are not limited to, an image and its category label, a question and its corresponding answer, or a manuscript and a human-generated editorial comment. Training records may be represented in structured or unstructured form, may include metadata, and may be stored in databases, files, or serialized objects. … more “Training record” or “Training records” refers to a collection of two-part data structures used to train a machine learning model. Each record comprises a first component (such as an input, stimulus, or query) and a second component (such as a corresponding label, target output, response, feedback, or the like), which are associated for the purpose of supervised, semi-supervised learning, or related machine-learning processes. Training records may be used to teach or train a model to associate certain types of inputs with corresponding outputs by adjusting internal parameters to minimize error between predicted and actual outputs. Examples include, but are not limited to, an image and its category label, a question and its corresponding answer, or a manuscript and a human-generated editorial comment. Training records may be represented in structured or unstructured form, may include metadata, and may be stored in databases, files, or serialized objects.

“Classification boundaries” refers to one or more decision thresholds, rules, learned parameters, or dividing functions used by a computational model to distinguish among different categories, labels, or outcome classes. Classification boundaries may be explicit (e.g., rule-based or threshold-based) or implicit (e.g., learned by a machine-learning model), and may operate in one or more dimensions of a feature space, embedding space, or probability distribution. In certain embodiments, classification boundaries are used by an artificial-intelligence system to determine how a manuscript or portion of a manuscript should be categorized relative to one or more screening or editorial outcomes, such as 'reject' or 'not reject'. … more “Classification boundaries” refers to one or more decision thresholds, rules, learned parameters, or dividing functions used by a computational model to distinguish among different categories, labels, or outcome classes. Classification boundaries may be explicit (e.g., rule-based or threshold-based) or implicit (e.g., learned by a machine-learning model), and may operate in one or more dimensions of a feature space, embedding space, or probability distribution. In certain embodiments, classification boundaries are used by an artificial-intelligence system to determine how a manuscript or portion of a manuscript should be categorized relative to one or more screening or editorial outcomes, such as 'reject' or 'not reject'.

“Manuscript screening outcomes” refers to the possible results, statuses, or dispositions that may be assigned to a manuscript during an editorial screening process. Examples of manuscript screening outcomes include, without limitation, rejection, no reject, acceptance for further review, request for revision, deferral, referral, or other editorial dispositions indicating whether or how the manuscript will proceed in a review workflow. Manuscript screening outcomes may be generated by human reviewers, automated systems, or a combination thereof. … more “Manuscript screening outcomes” refers to the possible results, statuses, or dispositions that may be assigned to a manuscript during an editorial screening process. Examples of manuscript screening outcomes include, without limitation, rejection, no reject, acceptance for further review, request for revision, deferral, referral, or other editorial dispositions indicating whether or how the manuscript will proceed in a review workflow. Manuscript screening outcomes may be generated by human reviewers, automated systems, or a combination thereof.

"Fixation," "fixation device," "fastener," or "fastener system" refers to an apparatus, instrument, structure, device, component, member, system, assembly, step, process, or module that is structured, configured, designed, arranged, or engineered to connect, join, engage, or couple two or more structures, either permanently or temporarily. The connected structures may be manmade and/or biological and may include hard tissues such as bone, teeth, or similar materials, as well as soft tissues such as ligaments, cartilage, tendons, or similar biological structures. In certain embodiments, fixation or fastening serves to secure two structures in a desired position and/or orientation, redistribute load or stress, maintain a desired level of tension or compression, and/or reduce relative motion between connected components. A fixation device or fastener may be made of metal, plastic, composite materials, metal alloys, plastic composites, biocompatible materials, biodegradable materials, or other suitable materials. In some embodiments, a fixation device or fastener may be part of a fastener system that includes two or more structures that work together to function as a fastening mechanism. For example, a fastener system may include a rod or shaft having external threads and an opening or bore within another structure having corresponding internal threads configured to engage the external threads of the rod or shaft. Fixation devices and fasteners may be used in internal or external fixation applications and may include, but are not limited to: screws, bone screws, set screws, rivets, bolts, nails, pins, Kirschner wires (K-wires), anchors, bone anchors, plates, bone plates, posts, thumb screws, nuts, intramedullary nails, rods, implants, sutures, soft sutures, soft anchors, tethers, interbody cages, fusion cages, staples, bone staples, hook-and-loop mechanisms, snaps, and similar structures. In certain embodiments, a fastener may include an adjective identifying an object or structure that the fastener is particularly configured, designed, or engineered to engage, connect, or couple with. For example, a "bone fastener" may refer to an apparatus for joining or connecting one or more bones, one or more bone portions, soft tissue and a bone or bone portion, hard tissue and a bone or bone portion, or an apparatus and a bone or portion of bone. In certain embodiments, a fastener may be a temporary fastener, meaning that the fastener is configured to serve a fastening function for a relatively short period of time. A temporary fastener may be used until another procedure or operation is completed and/or until a particular event occurs. A temporary fastener may be designed for removal by a user or may be configured to disengage due to an external event, structural change, or mechanical interaction. … more "Fixation," "fixation device," "fastener," or "fastener system" refers to an apparatus, instrument, structure, device, component, member, system, assembly, step, process, or module that is structured, configured, designed, arranged, or engineered to connect, join, engage, or couple two or more structures, either permanently or temporarily. The connected structures may be manmade and/or biological and may include hard tissues such as bone, teeth, or similar materials, as well as soft tissues such as ligaments, cartilage, tendons, or similar biological structures. In certain embodiments, fixation or fastening serves to secure two structures in a desired position and/or orientation, redistribute load or stress, maintain a desired level of tension or compression, and/or reduce relative motion between connected components. A fixation device or fastener may be made of metal, plastic, composite materials, metal alloys, plastic composites, biocompatible materials, biodegradable materials, or other suitable materials. In some embodiments, a fixation device or fastener may be part of a fastener system that includes two or more structures that work together to function as a fastening mechanism. For example, a fastener system may include a rod or shaft having external threads and an opening or bore within another structure having corresponding internal threads configured to engage the external threads of the rod or shaft. Fixation devices and fasteners may be used in internal or external fixation applications and may include, but are not limited to: screws, bone screws, set screws, rivets, bolts, nails, pins, Kirschner wires (K-wires), anchors, bone anchors, plates, bone plates, posts, thumb screws, nuts, intramedullary nails, rods, implants, sutures, soft sutures, soft anchors, tethers, interbody cages, fusion cages, staples, bone staples, hook-and-loop mechanisms, snaps, and similar structures. In certain embodiments, a fastener may include an adjective identifying an object or structure that the fastener is particularly configured, designed, or engineered to engage, connect, or couple with. For example, a "bone fastener" may refer to an apparatus for joining or connecting one or more bones, one or more bone portions, soft tissue and a bone or bone portion, hard tissue and a bone or bone portion, or an apparatus and a bone or portion of bone. In certain embodiments, a fastener may be a temporary fastener, meaning that the fastener is configured to serve a fastening function for a relatively short period of time. A temporary fastener may be used until another procedure or operation is completed and/or until a particular event occurs. A temporary fastener may be designed for removal by a user or may be configured to disengage due to an external event, structural change, or mechanical interaction.

As used herein, the terms “Coupling,” “Coupling Member,” and “Coupler” refer to one or more devices, components, members, interfaces, assemblies, structures, or combinations thereof that may connect, secure, attach, engage, retain, join, or otherwise facilitate coupling between two or more components, objects, assemblies, or structures. A coupler may provide a direct coupling between structures or an indirect coupling through one or more intermediate components or structures. A coupler may provide a fixed, movable, adjustable, removable, releasable, temporary, or permanent coupling and may permit or constrain relative movement between coupled structures. In certain embodiments, a coupler may be rigid, semi-rigid, flexible, semiflexible, pliable, elastic, resilient, articulated, or otherwise configured. A coupler may include, for example, one or more clamps, brackets, fasteners, hooks, catches, latches, pins, bolts, screws, straps, receivers, engagement members, connectors, interlocking structures, mounting interfaces, or the like. In certain embodiments, a coupler may engage an existing vehicle structure, mounting structure, or attachment feature. … more As used herein, the terms “Coupling,” “Coupling Member,” and “Coupler” refer to one or more devices, components, members, interfaces, assemblies, structures, or combinations thereof that may connect, secure, attach, engage, retain, join, or otherwise facilitate coupling between two or more components, objects, assemblies, or structures. A coupler may provide a direct coupling between structures or an indirect coupling through one or more intermediate components or structures. A coupler may provide a fixed, movable, adjustable, removable, releasable, temporary, or permanent coupling and may permit or constrain relative movement between coupled structures. In certain embodiments, a coupler may be rigid, semi-rigid, flexible, semiflexible, pliable, elastic, resilient, articulated, or otherwise configured. A coupler may include, for example, one or more clamps, brackets, fasteners, hooks, catches, latches, pins, bolts, screws, straps, receivers, engagement members, connectors, interlocking structures, mounting interfaces, or the like. In certain embodiments, a coupler may engage an existing vehicle structure, mounting structure, or attachment feature.

“Artificial Intelligence (AI) system” refers to a computational system, module, component, device, or arrangement of hardware, software, firmware, or combinations thereof configured to perform one and/or more tasks that may otherwise be performed by a human, including learning, inference, pattern recognition, prediction, classification, clustering, generation, optimization, planning, control, decision-making, or the like. An AI system may include one and/or more machine-learning models, neural networks, statistical models, rule-based engines, probabilistic models, symbolic reasoning components, or hybrid architectures. Example model types may include convolutional neural networks (CNNs), encoder–decoder architectures, transformer-based models, recurrent neural networks, generative models (including GANs, VAEs, diffusion models, autoregressive models), support-vector machines, decision trees, ensemble methods, Bayesian models, or the like. Synonyms include intelligent system, learning system, machine-learning system, AI module, cognitive system, or the like. … more “Artificial Intelligence (AI) system” refers to a computational system, module, component, device, or arrangement of hardware, software, firmware, or combinations thereof configured to perform one and/or more tasks that may otherwise be performed by a human, including learning, inference, pattern recognition, prediction, classification, clustering, generation, optimization, planning, control, decision-making, or the like. An AI system may include one and/or more machine-learning models, neural networks, statistical models, rule-based engines, probabilistic models, symbolic reasoning components, or hybrid architectures. Example model types may include convolutional neural networks (CNNs), encoder–decoder architectures, transformer-based models, recurrent neural networks, generative models (including GANs, VAEs, diffusion models, autoregressive models), support-vector machines, decision trees, ensemble methods, Bayesian models, or the like. Synonyms include intelligent system, learning system, machine-learning system, AI module, cognitive system, or the like.

"Repository" refers to a storage location, data store, collection, container, system, service, or other organized and/or accessible source of data and/or content that may be used for storage, retrieval, indexing, processing, distribution, synchronization, and/or management of information. A repository may reside locally, remotely, distributed across multiple systems, virtualized, cloud-based, and/or otherwise logically defined and may include one and/or more files, directories, databases, data structures, datasets, unified documents, services, and/or linked or referenced content. A repository may store structured data, semi-structured data, unstructured data, and/or metadata and may represent information in textual, binary, encoded, encrypted, compressed, proprietary, standardized, or other formats. Synonyms include data store, storage system, content store, information repository, data source, storage location, or the like. … more "Repository" refers to a storage location, data store, collection, container, system, service, or other organized and/or accessible source of data and/or content that may be used for storage, retrieval, indexing, processing, distribution, synchronization, and/or management of information. A repository may reside locally, remotely, distributed across multiple systems, virtualized, cloud-based, and/or otherwise logically defined and may include one and/or more files, directories, databases, data structures, datasets, unified documents, services, and/or linked or referenced content. A repository may store structured data, semi-structured data, unstructured data, and/or metadata and may represent information in textual, binary, encoded, encrypted, compressed, proprietary, standardized, or other formats. Synonyms include data store, storage system, content store, information repository, data source, storage location, or the like.

"Dataset" refers to a structured and/or unstructured collection, grouping, aggregation, or set of data instances, records, elements, objects, entries, or representations that may be organized according to one or more formats, schemas, models, relational structures, logical associations, or the like. A dataset may be used for training, testing, validation, retrieval, indexing, inference, analysis, generation, transformation, control, monitoring, or other computational and/or operational purposes and may include input data, output data, labeled and/or unlabeled data, paired data instances, linked data, streamed data, dynamically generated data, or the like. Synonyms include data collection, data set, data repository, data grouping, data corpus, information collection, or the like. … more "Dataset" refers to a structured and/or unstructured collection, grouping, aggregation, or set of data instances, records, elements, objects, entries, or representations that may be organized according to one or more formats, schemas, models, relational structures, logical associations, or the like. A dataset may be used for training, testing, validation, retrieval, indexing, inference, analysis, generation, transformation, control, monitoring, or other computational and/or operational purposes and may include input data, output data, labeled and/or unlabeled data, paired data instances, linked data, streamed data, dynamically generated data, or the like. Synonyms include data collection, data set, data repository, data grouping, data corpus, information collection, or the like.

"Language model" refers to a computational model that can be configured to process, generate, predict, transform, classify, summarize, or otherwise analyze linguistic or symbolic information. A language model may be implemented using neural network architectures, transformer-based architectures, machine learning systems, or other computational frameworks capable of modeling relationships among tokens, symbols, or representational units, or the like. A language model may operate in generative, discriminative, inferential, or analytical modes. Synonyms include generative model, neural language processor, sequence model, generative AI model, or the like. … more "Language model" refers to a computational model that can be configured to process, generate, predict, transform, classify, summarize, or otherwise analyze linguistic or symbolic information. A language model may be implemented using neural network architectures, transformer-based architectures, machine learning systems, or other computational frameworks capable of modeling relationships among tokens, symbols, or representational units, or the like. A language model may operate in generative, discriminative, inferential, or analytical modes. Synonyms include generative model, neural language processor, sequence model, generative AI model, or the like.

"Corpus" refers to a structured or unstructured body, collection, grouping, or aggregation of data, content, content items, informational material, or the like used for computational processing, analysis, retrieval, generation, training, validation, inference, comparison, evaluation, or the like. A corpus may include linguistic content, symbolic content, domain-specific content, documents, records, code samples, transcripts, labels, annotations, metadata, embeddings, structured datasets, tokenized sequences, vectorized representations, or the like and may be organized as raw text, indexed collections, linked structures, graph-based representations, datasets, or coordinated reference sets. A corpus may be curated by a user, assembled from public or proprietary sources, generated synthetically, dynamically constructed, or incrementally updated and may be deployed in training workflows, retrieval workflows, inferential workflows, generative workflows, or real-time processing environments. Synonyms include training corpus, retrieval corpus, language corpus, dataset, data collection, reference collection, information corpus, dataset collection, machine learning dataset, knowledge base, or the like. … more "Corpus" refers to a structured or unstructured body, collection, grouping, or aggregation of data, content, content items, informational material, or the like used for computational processing, analysis, retrieval, generation, training, validation, inference, comparison, evaluation, or the like. A corpus may include linguistic content, symbolic content, domain-specific content, documents, records, code samples, transcripts, labels, annotations, metadata, embeddings, structured datasets, tokenized sequences, vectorized representations, or the like and may be organized as raw text, indexed collections, linked structures, graph-based representations, datasets, or coordinated reference sets. A corpus may be curated by a user, assembled from public or proprietary sources, generated synthetically, dynamically constructed, or incrementally updated and may be deployed in training workflows, retrieval workflows, inferential workflows, generative workflows, or real-time processing environments. Synonyms include training corpus, retrieval corpus, language corpus, dataset, data collection, reference collection, information corpus, dataset collection, machine learning dataset, knowledge base, or the like.

"Review" refers to a process or set of actions by which submitted content, such as a manuscript, is examined, evaluated, or assessed for purposes such as quality control, compliance, relevance, completeness, originality, or suitability for publication. Synonyms for “review” may include evaluation, examination, critique, editorial analysis, inspection, vetting, or the like. The review may be performed manually by one or more individuals, such as editors or peer reviewers, or automatically by computing systems, including artificial intelligence models trained to simulate and/or support editorial decision-making. The review may include operations such as identifying strengths and weaknesses, applying scoring metrics, generating editorial comments, checking adherence to publisher guidelines, or determining whether the manuscript should proceed to further stages such as peer review, revision, acceptance or the like. In an artificial intelligence–assisted workflow, the review may further include processing manuscript content using machine learning techniques, generating a manuscript screening output, or comparing outcomes to historical or user-defined editorial standards. As used herein, ‘Review’ may also be referred to as, or encompass, evaluation, assessment, screening, triage, vetting, quality control, editorial examination, or the like. … more "Review" refers to a process or set of actions by which submitted content, such as a manuscript, is examined, evaluated, or assessed for purposes such as quality control, compliance, relevance, completeness, originality, or suitability for publication. Synonyms for “review” may include evaluation, examination, critique, editorial analysis, inspection, vetting, or the like. The review may be performed manually by one or more individuals, such as editors or peer reviewers, or automatically by computing systems, including artificial intelligence models trained to simulate and/or support editorial decision-making. The review may include operations such as identifying strengths and weaknesses, applying scoring metrics, generating editorial comments, checking adherence to publisher guidelines, or determining whether the manuscript should proceed to further stages such as peer review, revision, acceptance or the like. In an artificial intelligence–assisted workflow, the review may further include processing manuscript content using machine learning techniques, generating a manuscript screening output, or comparing outcomes to historical or user-defined editorial standards. As used herein, ‘Review’ may also be referred to as, or encompass, evaluation, assessment, screening, triage, vetting, quality control, editorial examination, or the like.

"Prompt" refers to an input signal, message, data structure, or the like provided to a computational system—such as an artificial intelligence system or large language model (LLM)—that initiates, guides, or influences the system’s generation, classification, or analysis of output. Synonyms for "prompt" include query, input query, user prompt, task directive, system instruction, or the like. A prompt may be expressed in natural language, code, structured syntax, formatted input, or embedded data representations, and may include one or more instructions, questions, or contextual inputs. The prompt may be static, dynamically generated, user-defined, or derived from prior interactions, system state, or metadata. In the context of machine learning and generative artificial intelligence, a prompt may shape or constrain the model’s inference behavior, including output tone, content domain, formatting, or level of specificity. Prompts may also be used during training, evaluation, or fine-tuning of models to simulate realistic tasks, enforce structure, or provide reference patterns, or the like. … more "Prompt" refers to an input signal, message, data structure, or the like provided to a computational system—such as an artificial intelligence system or large language model (LLM)—that initiates, guides, or influences the system’s generation, classification, or analysis of output. Synonyms for "prompt" include query, input query, user prompt, task directive, system instruction, or the like. A prompt may be expressed in natural language, code, structured syntax, formatted input, or embedded data representations, and may include one or more instructions, questions, or contextual inputs. The prompt may be static, dynamically generated, user-defined, or derived from prior interactions, system state, or metadata. In the context of machine learning and generative artificial intelligence, a prompt may shape or constrain the model’s inference behavior, including output tone, content domain, formatting, or level of specificity. Prompts may also be used during training, evaluation, or fine-tuning of models to simulate realistic tasks, enforce structure, or provide reference patterns, or the like.

"Training objective" refers to a defined goal, criterion, or set of conditions used to guide the learning process of an artificial intelligence model, machine learning system, or neural network during training. Synonyms for “training objective” may include optimization goal, learning criterion, loss function target, model supervision directive, or the like. The training objective may quantify how well the model performs a task and may direct the adjustment of model parameters to minimize or maximize a defined measure of performance. In some embodiments, the training objective may incorporate one or more performance metrics, similarity comparisons, classification accuracy measures, or quality-based evaluations. In the context of editorial content generation, the training objective may include comparing a machine-generated editorial comment for a manuscript to a human-generated editorial comment for the same manuscript using a similarity metric such as cosine similarity, thereby encouraging the model to produce outputs that align semantically with human-provided output. The training objective may also include additional components such as language fluency, coherence, informativeness, or compliance with editorial guidelines. The training objective may be used in supervised, semi-supervised, or reinforcement learning workflows and may be updated or tuned over time to reflect evolving editorial standards, reviewer preferences, or corpus characteristics. In certain embodiments, a composite training objective may be employed. A composite training objective refers to a training objective that includes two or more individual objective components that are optimized jointly, such as a combination of classification accuracy, semantic similarity, language quality, or rule-based compliance. The individual components of a composite training objective may be combined through weighting, aggregation, or other optimization strategies, and may be adjusted over time to balance competing editorial goals or performance considerations. … more "Training objective" refers to a defined goal, criterion, or set of conditions used to guide the learning process of an artificial intelligence model, machine learning system, or neural network during training. Synonyms for “training objective” may include optimization goal, learning criterion, loss function target, model supervision directive, or the like. The training objective may quantify how well the model performs a task and may direct the adjustment of model parameters to minimize or maximize a defined measure of performance. In some embodiments, the training objective may incorporate one or more performance metrics, similarity comparisons, classification accuracy measures, or quality-based evaluations. In the context of editorial content generation, the training objective may include comparing a machine-generated editorial comment for a manuscript to a human-generated editorial comment for the same manuscript using a similarity metric such as cosine similarity, thereby encouraging the model to produce outputs that align semantically with human-provided output. The training objective may also include additional components such as language fluency, coherence, informativeness, or compliance with editorial guidelines. The training objective may be used in supervised, semi-supervised, or reinforcement learning workflows and may be updated or tuned over time to reflect evolving editorial standards, reviewer preferences, or corpus characteristics. In certain embodiments, a composite training objective may be employed. A composite training objective refers to a training objective that includes two or more individual objective components that are optimized jointly, such as a combination of classification accuracy, semantic similarity, language quality, or rule-based compliance. The individual components of a composite training objective may be combined through weighting, aggregation, or other optimization strategies, and may be adjusted over time to balance competing editorial goals or performance considerations.

“Editorial comment” refers to a written response, observation, critique, suggestion, or assessment relating to a manuscript or other content, typically intended to provide feedback, guidance, or evaluation. An editorial comment may address aspects of the content’s structure, format, tone, approach, methodology, clarity, style, or the like. Editorial comments may be used in peer review, editorial screening, manuscript assessment, or the like, and may be authored at least in part by a person such as an editor or reviewer or generated by a system such as a machine learning model or rule-based engine. An editorial comment may be authored in whole or in part by a human editor, reviewer, or other participant in the review process, or may be generated in whole or in part by a computing system, including a rule-based engine or machine-learning model. Unless expressly stated otherwise, the term “editorial comment” encompasses both human-generated and machine-generated comments, as well as comments collaboratively produced by human and machine systems. … more “Editorial comment” refers to a written response, observation, critique, suggestion, or assessment relating to a manuscript or other content, typically intended to provide feedback, guidance, or evaluation. An editorial comment may address aspects of the content’s structure, format, tone, approach, methodology, clarity, style, or the like. Editorial comments may be used in peer review, editorial screening, manuscript assessment, or the like, and may be authored at least in part by a person such as an editor or reviewer or generated by a system such as a machine learning model or rule-based engine. An editorial comment may be authored in whole or in part by a human editor, reviewer, or other participant in the review process, or may be generated in whole or in part by a computing system, including a rule-based engine or machine-learning model. Unless expressly stated otherwise, the term “editorial comment” encompasses both human-generated and machine-generated comments, as well as comments collaboratively produced by human and machine systems.

“Screening Decision” refers to the preliminary editorial determination made to assess whether a submitted manuscript meets certain criteria for further consideration. The determination may be made by a journal or editorial team with or without the assistance of software and/or computer systems. The screening decision may be based on subjective criteria (e.g., perceived novelty, clarity of presentation, and editorial fit) and/or objective criteria (e.g., adherence to formatting rules, completeness of statistical reporting, detection of plagiarism, confirmation of required ethical disclosures, and validation of authorship metadata). The criteria may include scope, relevance, content quality (clarity, methodology), novelty, research relevance, research timeliness, academic rigor, compliance with submission guidelines such as ethical requirements and study requirements, and/or other editorial standards. The Screening Decision may and typically does occur before formal peer review and may result in immediate rejection, acceptance for review, requests for modification, or the like. The term encompasses a range of synonymous or closely related practices, including editorial triage, desk decisions, gatekeeping determinations, and initial suitability reviews. In certain embodiments, a Screening Decision is a preliminary decision made as part of a larger manuscript review process for a given manuscript. In another embodiment, is a final decision made a given manuscript, which may or may not be reviewed by a human editor before notice of the Screening Decision is sent to the author. A screening decision may be generated in whole or in part by a human editor, associate editor, or reviewer, or may be generated in whole or in part by a computing system, including a rule-based engine or machine-learning model. The term “screening decision” encompasses both human-generated and machine-generated screening determinations unless expressly stated otherwise. … more “Screening Decision” refers to the preliminary editorial determination made to assess whether a submitted manuscript meets certain criteria for further consideration. The determination may be made by a journal or editorial team with or without the assistance of software and/or computer systems. The screening decision may be based on subjective criteria (e.g., perceived novelty, clarity of presentation, and editorial fit) and/or objective criteria (e.g., adherence to formatting rules, completeness of statistical reporting, detection of plagiarism, confirmation of required ethical disclosures, and validation of authorship metadata). The criteria may include scope, relevance, content quality (clarity, methodology), novelty, research relevance, research timeliness, academic rigor, compliance with submission guidelines such as ethical requirements and study requirements, and/or other editorial standards. The Screening Decision may and typically does occur before formal peer review and may result in immediate rejection, acceptance for review, requests for modification, or the like. The term encompasses a range of synonymous or closely related practices, including editorial triage, desk decisions, gatekeeping determinations, and initial suitability reviews. In certain embodiments, a Screening Decision is a preliminary decision made as part of a larger manuscript review process for a given manuscript. In another embodiment, is a final decision made a given manuscript, which may or may not be reviewed by a human editor before notice of the Screening Decision is sent to the author. A screening decision may be generated in whole or in part by a human editor, associate editor, or reviewer, or may be generated in whole or in part by a computing system, including a rule-based engine or machine-learning model. The term “screening decision” encompasses both human-generated and machine-generated screening determinations unless expressly stated otherwise.

“User-defined corpus” or “Human-defined corpus” refers to a dataset that includes training data records selected, curated, or otherwise designated or prepared by a user and/or system administrator. Each training record may include a human generated/authored manuscript by a third-party an author, a corresponding human-generated/authored editorial comment produced for that manuscript, and and/or a corresponding human-generated/authored screening decision produced for that same manuscript. In such one an embodiment, the manuscript may serve as the input for two labels, one label for the screening decision and the other label for the editorial comment. Advantageously, the corpus may reflect domain-specific, publisher specific, and/or publication-specific editorial preferences, metrics and/or criteria and is used to train the artificial intelligence module described herein. … more “User-defined corpus” or “Human-defined corpus” refers to a dataset that includes training data records selected, curated, or otherwise designated or prepared by a user and/or system administrator. Each training record may include a human generated/authored manuscript by a third-party an author, a corresponding human-generated/authored editorial comment produced for that manuscript, and and/or a corresponding human-generated/authored screening decision produced for that same manuscript. In such one an embodiment, the manuscript may serve as the input for two labels, one label for the screening decision and the other label for the editorial comment. Advantageously, the corpus may reflect domain-specific, publisher specific, and/or publication-specific editorial preferences, metrics and/or criteria and is used to train the artificial intelligence module described herein.