Improving Weight of Evidence Approaches to Chemical Evaluations
Bibliographic record
Abstract
Federal and other regulatory agencies often use or claim to use a weight of evidence (WoE) approach in chemical evaluation. Their approaches to the use of WoE, however, differ significantly, rely heavily on subjective professional judgment, and merit improvement. We review uses of WoE approaches in key articles in the peer-reviewed scientific literature, and find significant variations. We find that a hypothesis-based WoE approach, developed by Lorenz Rhomberg et al., can provide a stronger scientific basis for chemical assessment while improving transparency and preserving the appropriate scope of professional judgment. Their approach, while still evolving, relies on the explicit specification of the hypothesized basis for using the information at hand to infer the ability of an agent to cause human health impacts or, more broadly, affect other endpoints of concern. We describe and endorse such a hypothesis-based WoE approach to chemical evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.577 | 0.811 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.053 | 0.025 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".