The need for good practice in the application of mechanistic constructs in hazard and risk assessment
Bibliographic record
Abstract
A series of recent and proposed workshops address the interface between key characteristics and mechanistic pathway descriptions (adverse outcome pathways and mode of action) to identify commonalities and potential for complementary application. Informed by different communities, these constructs have collective potential to increase confidence to support the application of mechanistic data in hazard assessment. This forum article summarizes concepts, introduces evolving understanding, and invites future collaboration to contribute to better common understanding and development of good practice in the use of mechanistic data in hazard assessment.
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 | 0.508 | 0.374 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.008 | 0.076 |
| Scholarly communication | 0.031 | 0.058 |
| Open science | 0.023 | 0.029 |
| Research integrity | 0.029 | 0.077 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".