Evaluation reports for TEBT-2023-0011 - Svendsen et al.
Bibliographic record
Abstract
Evaluation report for "Cross-mapping of terms used in chemical risk assessment with 1 those used in systematic review: research protocol" (Svendsen et al). This manuscript describes a protocol for cross-mapping core terms used by the systematic review and chemical risk assessment communities to improve inter-operability and communication. The proposed method originally consised of drafting a long list of terms using Term Frequency - Inverse Document Frequency (TF-IDF) method in the first phase, followed by three consecutive phases using consensus-based approaches to derive definitions of terms and identify overlaps. Editorial triage found that overall the objective, approach, and plan of analysis were clear and sound. The only suggestions to improve transparency was to add further detail on any text mining tool employed for the first phase and extend the consideration of lack of representation within recruited experts beyond specific institutions to also consider personal characteristics such as age, gender, race etc. The first round of peer review generated very valuable and detailed comments on the proposed methodology that highlight where detail on specific steps and/or justification for the selection of a specific approach is lacking, including grounding the protocol on a solid up-to-date understanding of methods for consensus building and controlled vocabulary development. Some more fundamental questions were raised about the symmetry of the translation effort and the reciprocity of the exchange between the two communities. The authors have clearly engaged with the peer review process and reflected on their proposed methods. The authors piloted the originally proposed Term Frequency - Inverse Document Frequency (TF-IDF) method on the document collections. As it did not perform as expected, a new and simpler manual approach has been suggested. As a result, the proposed methods have been amended and simplified. The authors have also provided detailed answers to comments and in most instances have amended the manuscript accordingly. During the second round of peer review, reviewers expressed their satisfaction with simplified methods. A few remaining issues have been addressed in responses to reviewers for which it would be desirable also to amend the text to improve transparency. The authors did as suggested and submitted the final version of the manuscript which was accepted. The following points of scientific interest were surfaced by the review process; first, piloting planned methods whenever feasible is worthwhile exercise that may lead to simpler, improved methods, secondly, the perspective of reviewers from other academic disciplines on similar issues was very useful and allowed authors to benefit from experience gained in those disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.327 | 0.542 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.139 | 0.035 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".