The role of QSARs and fate models in chemical hazard and risk assessment
Bibliographic record
Abstract
Abstract A structure is suggested and discussed for the assessment of hazard and risk of chemicals of commerce, starting from a knowledge of molecular structure and proceeding to estimation of chemical properties, environmental fate and presence in organisms. Two metrics of risk are described, the external risk ratio which is based on concentrations external to the organism and the internal risk ratio based on concentrations internal to the organism. Where possible, the latter is preferred. Aspects of this multi‐stage strategy are discussed in more detail including the need for more experimental data in support of QSARs, the need for consistency in QSARs describing related properties and the complementary roles of fate models and QSARs. Whereas most screening‐level regulatory assessments of large numbers of chemicals focus on hazard, it is argued that the public concern is primarily with risk. Since risk assessment depends on the availability of data on rates of emission and such data are often very uncertain, this stage is often delayed and may only be done for relatively few substances. This is unfortunate because many hazardous substances are used under conditions such that there is minimal risk of exposure and effects. It is suggested that risk assessment can be facilitated by “backtracking” from an arbitrarily assumed risk ratio to calculate a hypothetical “critical” emission rate which would support that ratio. This rate can then be compared with likely emission to give an indication of proximity to levels of concern and thus the sustainability of present chemical emission practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".