The role of QSARs and fate models in chemical hazard and risk assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it