“Editorial review” refers to a process, workflow, or decision-making activity in which one or more authorized reviewers evaluate a manuscript, its associated metadata, or outputs generated by a screening method, apparatus, or system, for purposes including suitability assessment, compliance checking, quality control, or publication decision support. Editorial review may include examining manuscript content, reviewing automated screening outputs, modifying recommendations, entering comments, requesting revisions, forwarding for peer review, or making preliminary accept–reject determinations. Editorial review may be performed by editors, associate editors, editorial assistants, reviewers, or other designated personnel using software tools, automated systems, methods, apparatuses, or a combination thereof. As used herein, ‘editorial review’ refers to and includes any form of manuscript evaluation, assessment, screening, triage, vetting, quality control, or decision support, whether performed by a human, an artificial intelligence system, or a combination thereof, and whether occurring before, during, or after peer review. … more “Editorial review” refers to a process, workflow, or decision-making activity in which one or more authorized reviewers evaluate a manuscript, its associated metadata, or outputs generated by a screening method, apparatus, or system, for purposes including suitability assessment, compliance checking, quality control, or publication decision support. Editorial review may include examining manuscript content, reviewing automated screening outputs, modifying recommendations, entering comments, requesting revisions, forwarding for peer review, or making preliminary accept–reject determinations. Editorial review may be performed by editors, associate editors, editorial assistants, reviewers, or other designated personnel using software tools, automated systems, methods, apparatuses, or a combination thereof. As used herein, ‘editorial review’ refers to and includes any form of manuscript evaluation, assessment, screening, triage, vetting, quality control, or decision support, whether performed by a human, an artificial intelligence system, or a combination thereof, and whether occurring before, during, or after peer review.

“Edit feature” refers to any software functionality, user interface control, system capability, or workflow mechanism that allows an authorized user to modify, adjust, annotate, override, supplement, or otherwise change content or parameters associated with operation of screening method, system, or apparatus. An edit feature may enable modification of a manuscript screening output, a prompt, a screening prompt, or instruction used to generate an output, editorial guidance text, classification selections, system recommendations, review criteria, or similar elements. In some embodiments, an edit feature supports iterative refinement, permitting a user to make revisions, trigger re-analysis, or request alternative outputs based on the edited content. … more “Edit feature” refers to any software functionality, user interface control, system capability, or workflow mechanism that allows an authorized user to modify, adjust, annotate, override, supplement, or otherwise change content or parameters associated with operation of screening method, system, or apparatus. An edit feature may enable modification of a manuscript screening output, a prompt, a screening prompt, or instruction used to generate an output, editorial guidance text, classification selections, system recommendations, review criteria, or similar elements. In some embodiments, an edit feature supports iterative refinement, permitting a user to make revisions, trigger re-analysis, or request alternative outputs based on the edited content.

“Metadata inconsistencies” refers to mismatches or irregularities in structured information associated with a manuscript submission. Metadata inconsistencies may include conflicting submission dates, mismatched author affiliations, inconsistent correspondence addresses, unusual revision timelines, conflicting declarations, or similar irregular metadata characteristics. … more “Metadata inconsistencies” refers to mismatches or irregularities in structured information associated with a manuscript submission. Metadata inconsistencies may include conflicting submission dates, mismatched author affiliations, inconsistent correspondence addresses, unusual revision timelines, conflicting declarations, or similar irregular metadata characteristics.

“Authorship anomalies” refers to unusual, inconsistent, or suspicious author-related characteristics observed across manuscripts, submissions, records, or the like. Authorship anomalies may include frequent author name changes, sudden topic shifts inconsistent with prior expertise, mismatched affiliations, mismatched identifiers, repeated identical contributor statements, or similar irregular authorship patterns. … more “Authorship anomalies” refers to unusual, inconsistent, or suspicious author-related characteristics observed across manuscripts, submissions, records, or the like. Authorship anomalies may include frequent author name changes, sudden topic shifts inconsistent with prior expertise, mismatched affiliations, mismatched identifiers, repeated identical contributor statements, or similar irregular authorship patterns.

“Paper-mill generated manuscripts” refers to manuscripts produced, sold, or distributed by entities that generate publications or authorship opportunities for profit rather than legitimate scholarship. Paper-mill manuscripts may display repeated templates, reused figures, generic methods sections, fabricated data, recycled phrasing, or other systematic artifacts of mass-produced submissions. … more “Paper-mill generated manuscripts” refers to manuscripts produced, sold, or distributed by entities that generate publications or authorship opportunities for profit rather than legitimate scholarship. Paper-mill manuscripts may display repeated templates, reused figures, generic methods sections, fabricated data, recycled phrasing, or other systematic artifacts of mass-produced submissions.

“Classification accuracy” refers to a measure of how correctly a computational system assigns items to their intended categories or outcome classes. Classification accuracy may be calculated in various ways, including, without limitation, the proportion of correctly classified samples, weighted or averaged accuracy across multiple classes, accuracy computed relative to a reference dataset containing known or expected labels, or the like. In some embodiments, classification accuracy is used as part of a training objective, performance evaluation process, quality-control mechanism for manuscript screening systems, or the like. … more “Classification accuracy” refers to a measure of how correctly a computational system assigns items to their intended categories or outcome classes. Classification accuracy may be calculated in various ways, including, without limitation, the proportion of correctly classified samples, weighted or averaged accuracy across multiple classes, accuracy computed relative to a reference dataset containing known or expected labels, or the like. In some embodiments, classification accuracy is used as part of a training objective, performance evaluation process, quality-control mechanism for manuscript screening systems, or the like.

“Rogue-author behavior” refers to conduct by an individual or group intended to manipulate, circumvent, or exploit the scholarly publication process. Rogue-author behavior may include fabricated data, false authorship declarations, purchased authorship, ghostwritten submissions, undisclosed conflicts, plagiarism, or similar academic misconduct. … more “Rogue-author behavior” refers to conduct by an individual or group intended to manipulate, circumvent, or exploit the scholarly publication process. Rogue-author behavior may include fabricated data, false authorship declarations, purchased authorship, ghostwritten submissions, undisclosed conflicts, plagiarism, or similar academic misconduct.

“Completeness-audit” refers to an automated or semi-automated process configured to evaluate whether a manuscript contains information that would ordinarily be expected based on the study design, editorial guidelines, or applicable reporting standards. A completeness-audit may examine the presence of disclosure elements, limitation discussions, sample-size justifications, statistical reporting details, or other expected components of scholarly reporting. In some embodiments, a completeness-audit identifies sections that are missing, incomplete, or inconsistent with other reported information. … more “Completeness-audit” refers to an automated or semi-automated process configured to evaluate whether a manuscript contains information that would ordinarily be expected based on the study design, editorial guidelines, or applicable reporting standards. A completeness-audit may examine the presence of disclosure elements, limitation discussions, sample-size justifications, statistical reporting details, or other expected components of scholarly reporting. In some embodiments, a completeness-audit identifies sections that are missing, incomplete, or inconsistent with other reported information.

“Statistical results” refers to outcomes, estimates, measurements, or analytical findings produced through the application of statistical methods to a dataset. Statistical results may include values such as means, proportions, regression coefficients, p-values, confidence intervals, effect sizes, test statistics, or probability estimates. Statistical results may be expressed in narrative text, tables, figures, computational outputs, or the like. … more “Statistical results” refers to outcomes, estimates, measurements, or analytical findings produced through the application of statistical methods to a dataset. Statistical results may include values such as means, proportions, regression coefficients, p-values, confidence intervals, effect sizes, test statistics, or probability estimates. Statistical results may be expressed in narrative text, tables, figures, computational outputs, or the like.

“Similarity” refers to a quantitative or qualitative measure indicating a degree of relatedness, correspondence, resemblance, alignment, or the like between two items, such as two texts, vectors, features, model outputs, or the like. Similarity may be assessed using numerical metrics (e.g., cosine similarity, correlation, distance-based measures, or the like) or rule-based or semantic comparison techniques. In certain embodiments, similarity is used to compare machine-generated content to human-generated content, to evaluate consistency of outputs across models, or to guide training or optimization processes. … more “Similarity” refers to a quantitative or qualitative measure indicating a degree of relatedness, correspondence, resemblance, alignment, or the like between two items, such as two texts, vectors, features, model outputs, or the like. Similarity may be assessed using numerical metrics (e.g., cosine similarity, correlation, distance-based measures, or the like) or rule-based or semantic comparison techniques. In certain embodiments, similarity is used to compare machine-generated content to human-generated content, to evaluate consistency of outputs across models, or to guide training or optimization processes.

“Statistical reporting details” refers to numerical, descriptive, or analytical information that clarifies how statistical analyses were conducted or interpreted. Statistical reporting details may include confidence intervals, test statistics, variance measures, assumptions checked, correction procedures, model specifications, or similar components of statistical description. … more “Statistical reporting details” refers to numerical, descriptive, or analytical information that clarifies how statistical analyses were conducted or interpreted. Statistical reporting details may include confidence intervals, test statistics, variance measures, assumptions checked, correction procedures, model specifications, or similar components of statistical description.

“Sample-size justification” refers to an explanation, calculation, rationale, or discussion regarding how the sample size of a study was determined or why it is appropriate for the intended analyses. Sample-size justification may include power calculations, effect-size assumptions, feasibility explanations, economic constraints, references to prior-published benchmarks, or the like. … more “Sample-size justification” refers to an explanation, calculation, rationale, or discussion regarding how the sample size of a study was determined or why it is appropriate for the intended analyses. Sample-size justification may include power calculations, effect-size assumptions, feasibility explanations, economic constraints, references to prior-published benchmarks, or the like.

“Limitation discussion” refers to narrative text in which the authors describe weaknesses, constraints, assumptions, or uncertainties associated with their study. A limitation discussion may address matters such as sample size limits, potential biases, measurement constraints, confounding factors, generalizability restrictions, or similar study limitations. … more “Limitation discussion” refers to narrative text in which the authors describe weaknesses, constraints, assumptions, or uncertainties associated with their study. A limitation discussion may address matters such as sample size limits, potential biases, measurement constraints, confounding factors, generalizability restrictions, or similar study limitations.

“Disclosure elements” refers to statements, sections, declarations, or informational components in a manuscript that communicate procedural transparency, ethical compliance, limitations, funding context, or authorship-related information. Disclosure elements may include statements regarding conflicts of interest, ethical approvals, funding sources, contributor roles, data availability, prior publication, or similar disclosures. … more “Disclosure elements” refers to statements, sections, declarations, or informational components in a manuscript that communicate procedural transparency, ethical compliance, limitations, funding context, or authorship-related information. Disclosure elements may include statements regarding conflicts of interest, ethical approvals, funding sources, contributor roles, data availability, prior publication, or similar disclosures.

“Evidentiary support” refers to the data, analytical results, citations, or reasoning that substantiate a claim made in a manuscript. Evidentiary support may include statistical results, experimental outcomes, prior-published research, empirical observations, analytical derivations, or the like. Evidentiary support may appear in figures, tables, narrative text, appendices, or supporting documentation. … more “Evidentiary support” refers to the data, analytical results, citations, or reasoning that substantiate a claim made in a manuscript. Evidentiary support may include statistical results, experimental outcomes, prior-published research, empirical observations, analytical derivations, or the like. Evidentiary support may appear in figures, tables, narrative text, appendices, or supporting documentation.

“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.

“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'.

“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.

“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.

"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.