On the validity of classifying chemicals for persistence, bioaccumulation, toxicity, and potential for long-range transport
Bibliographic record
Abstract
It is argued that chemical substances can be meaningfully ranked or classified according to their persistence (P), bioaccumulation (B), toxicity (T), and potential for long-range transport (LRT) only if these attributes can be shown to be intensive, as distinct from extensive, properties of the substance, i.e., they are independent of quantity of substance. It is shown that P, B, and LRT can be considered intensive or quasi-intensive properties, but toxicity is more problematic. To obtain an intensive metric of toxicity requires selection of one of several possible extensive quantities that define exposure or dose. Ranking of a group of chemicals by toxicity is shown to be very dependent on which quantity is selected. It is suggested that toxicity metrics, such as lethal concentration to 50% of the population (LC50), lethal dose to 50% of the population (LD50), and threshold limit value (TLV) suffer the severe disadvantage of being dependent on the efficiency of delivery of the substance to the site(s) of toxic action in the organism. The use of measured or calculated internal dose is a preferable measure of toxicity since it reduces ambiguities inherent in the other metrics. Also, the primary concern is not the quasi-intensive property of toxicity; rather, it is the risk of toxic effects, an extensive quantity. To adequately assess the risk of toxic effects, both the toxic hazard and the degree of exposure must be characterized. Since exposure cannot be estimated without knowledge of the emission rate of chemicals to the environment, a compelling case can be made that screening to identify priority P, B, T, and LRT substances should be expanded to include quantity released to the environment as an additional factor.
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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 | 0.047 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".