Influence of exposure and toxicokinetics on measures of aquatic toxicity for organic contaminants: A case study review
Bibliographic record
Abstract
This theoretical and case study review of dynamic exposures of aquatic organisms to organic contaminants examines variables important for interpreting exposure and therefore toxicity. The timing and magnitude of the absorbed dose change when the dynamics of exposure change. Thus, the dose metric for interpreting toxic responses observed during such exposure conditions is generally limited to the specific experiment and cannot be extrapolated to either other experiments with different exposure dynamics or to field exposures where exposure dynamics usually are different. This is particularly true for mixture exposures, for which the concentration and composition and, therefore, the timing and magnitude of exposure to individual components of different potency and potentially different mechanisms of action can vary. Aquatic toxicology needs studies that develop temporal thresholds for absorbed toxicant doses to allow for better extrapolation between conditions of dynamic exposure. Improved experimental designs are required that include high-quality temporal measures of both the exposure and the absorbed dose to allow better interpretation of data. For the short term, initial water concentration can be considered a conservative measure of exposure, although the extent to which this is true cannot be estimated specifically unless the dynamics of exposure as well as the toxicokinetics of the chemicals in the exposure scenario for the organism of interest are known. A better, but still limited, metric for interpreting the exposure and, therefore, toxicity is the peak absorbed dose, although this neglects toxicodynamics, requires appropriate temporal measures of accumulated dose to determine the peak concentration, and requires temporal thresholds for critical body residue for each component of the mixture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".