Evaluation of the Inherent Toxicity Concept in Environmental Toxicology and Risk Assessment
Bibliographic record
Abstract
Intrinsic/inherent chemical properties are characteristic, irrespective of the number of molecules present. However, toxicity is an extensive/extrinsic biochemical property that depends on the number of molecules. Paracelsus, often considered the father of toxicology, noted that all things are poisonous. Because dose magnitude (i.e., number of molecules) determines the occurrence of poisonous effects, toxicity cannot be an intrinsic/inherent biochemical property. Thus, toxicology's task is to determine case-specific risks resulting in adverse effects produced by the interaction of toxic doses/exposures, toxic mechanisms, and case-specific influencing factors. Experimental testing results are known to vary within and between chemicals, test organisms, and experimental conditions and repetitions; however, hazard-based approaches treat toxicity as a fixed and constant property. A logical alternative is the standard-risk, case-specific risk model. In this approach, testing data are defined as standard risks where the nature, magnitude, and toxicity effect is standardized to the organism, chemical, and test conditions. Interpolation/extrapolation of standard risks to site-specific conditions (i.e., case-specific risks) is challenging, requiring understanding of the influences of the complex interactions within and between differing species, conditions, and toxicity-modifying factors. Therefore, Paracelsus's paradigm is perhaps better abbreviated as "dose-causality-response", because a key interpretive requirement is establishing toxicity causality by separating mode/mechanism of toxic action from modifying factor influences in overall toxicity responses. Unfortunately, the current knowledge base is inadequate. Moving to a standard-risk-specific-risk paradigm would highlight the importance of improving the toxicity causality knowledge base. Thereby, a rationale would be provided for enhancing the design and interpretation of toxicity testing that is necessary for achieving advances in routine translation of standard-risk to specific-risk estimates-the raison d'être of regulatory risk decision making. Environ Toxicol Chem 2020;39:2351-2360. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.053 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".