Evaluation of critical body residue data for acute narcosis in aquatic organisms
Bibliographic record
Abstract
The Environmental Residue Effects Database was evaluated to identify critical body residues of organic chemicals causing acute baseline neutral narcosis in aquatic organisms. Over 15 000 records for >400 chemicals were evaluated. Mean molar critical body residues in the final data set of 161 records for 29 chemicals were within published ranges but varied within and among chemicals and species (~3 orders of magnitude), and lipid normalization did not consistently decrease variability. All 29 chemicals can act as baseline neutral narcotics, but chemicals and/or their metabolites may also act by nonnarcotic modes of action. Specifically, nonnarcotic toxicity of polycyclic aromatic hydrocarbons and/or their biotransformation derivatives may be a significant source of variability. Complete testing of the narcosis-critical body residue hypothesis was confounded by data gaps for key toxicity modifying factors such as metabolite formation/toxicity, lipid content/composition, other modes of toxic action, and lack of steady-state status. Such problems impede determination of the precise, accurate toxicity estimates necessary for sound toxicological comparisons. Thus, neither the data nor the chemicals in the final data set should be considered definitive. Changes to testing designs and methods are necessary to improve data collection and critical body residue interpretation for hazard and risk assessment. Each of the toxicity metrics discussed-wet weight and lipid weight critical body residues, volume fraction in organism lipid, and chemical activity-has advantages, but all are subject to the same toxicity modifying factors.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".