Advancing environmental toxicology through chemical dosimetry: External exposures versus tissue residues
Bibliographic record
Abstract
The tissue residue dose concept has been used, although in a limited manner, in environmental toxicology for more than 100 y. This review outlines the history of this approach and the technical background for organic chemicals and metals. Although the toxicity of both can be explained in tissue residue terms, the relationship between external exposure concentration, body and/or tissues dose surrogates, and the effective internal dose at the sites of toxic action tends to be more complex for metals. Various issues and current limitations related to research and regulatory applications are also examined. It is clear that the tissue residue approach (TRA) should be an integral component in future efforts to enhance the generation, understanding, and utility of toxicity testing data, both in the laboratory and in the field. To accomplish these goals, several key areas need to be addressed: 1) development of a risk-based interpretive framework linking toxicology and ecology at multiple levels of biological organization and incorporating organism-based dose metrics; 2) a broadly applicable, generally accepted classification scheme for modes/mechanisms of toxic action with explicit consideration of residue information to improve both single chemical and mixture toxicity data interpretation and regulatory risk assessment; 3) toxicity testing protocols updated to ensure collection of adequate residue information, along with toxicokinetics and toxicodynamics information, based on explicitly defined toxicological models accompanied by toxicological model validation; 4) continued development of residue-effect databases is needed ensure their ongoing utility; and 5) regulatory guidance incorporating residue-based testing and interpretation approaches, essential in various jurisdictions.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".