Parsing the toxicity paradox: Composition and duration of exposure alter predicted oil spill effects by orders of magnitude
Bibliographic record
Abstract
Oil spilled into an aquatic environment produces oil droplet and dissolved component concentrations and compositions that are highly variable in space and time. Toxic effects on aquatic biota vary with sensitivity of the organism, concentration, composition, environmental conditions, and frequency and duration of exposure to the mixture of oil-derived dissolved compounds. For a range of spill (surface, subsea, blowout) and oil types under different environmental conditions, modeling of oil transport, fate, and organism behavior was used to quantify expected exposures over time for planktonic, motile, and stationary organisms. Different toxicity models were applied to these exposure time histories to characterize the influential roles of composition, concentration, and duration of exposure on aquatic toxicity. Misrepresenting these roles and exposures can affect results by orders of magnitude. Well-characterized laboratory studies for <24-hour exposures are needed to improve toxicity predictions of the typically short-term exposures that characterize spills.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".