Relationships between exposure and dose in aquatic toxicity tests for organic chemicals
Bibliographic record
Abstract
There is continuing debate about the merits of exposure-based toxicity metrics such as median lethal concentration (LC50) versus organism-based metrics such as critical body residue (CBR) as indicators of chemical toxicity to aquatic organisms. To demonstrate relationships and differences between these 2 metrics, the authors applied a simple one-compartment toxicokinetic mass-balance model for water-exposed fish for a series of hypothetical organic chemicals exhibiting baseline narcotic toxicity. The authors also considered the influence of several toxicity-modifying factors. The results showed that the results of standard toxicity tests, such as the LC50, are strongly influenced by several modifying factors, including chemical and organism characteristics such as hydrophobicity, body size, lipid content, metabolic biotransformation, and exposure durations. Consequently, reported LC50s may not represent consistent dose surrogates and may be inappropriate for comparing the relative toxicity of chemicals. For comparisons of toxicity between chemicals, it is preferable to employ a delivered dose metric, such as the CBR. Reproducible toxicity data for a specific combination of chemical, exposure conditions, and organism can be obtained only if the extent of approach to steady state is known. Suggestions are made for revisions in test protocols, including the use of models in advance of empirical testing, to improve the efficiency and effectiveness of tests and reduce the confounding influences of toxicity-modifying factors, especially exposure duration and metabolic biotransformation. This will assist in linking empirical measurements of LC50s and CBRs, 2 different but related indicators of aquatic toxicity, and thereby improve understanding of the large existing database of aquatic toxicity test results.
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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 | 0.034 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".