Using field data to assess the effects of pesticides on crustacea in freshwater aquatic ecosystems and verifying the level of protection provided by water quality guidelines
Bibliographic record
Abstract
The purpose of this study was to investigate how well single-species laboratory data predict real-world pesticide toxicity effects on Crustacea. Data from field pesticide exposures from experimental mesocosm and small pond studies were converted into toxicity units (TUs) by dividing measured pesticide concentrations by the L(E)C50 for Daphnia or acute 5% hazard concentration for Crustacea (HC5-C). The proportion of crustacean taxa significantly affected by the pesticide treatment, called the count ratio of effect, was used in logistic regression models. Of 200 possible logistic model combinations of the TUs, fate, physicochemical variables, and structural variables versus the count ratio of effect for the mesocosm data, the best model was found to incorporate log(TU HC5-C). This model was used to convert pesticide water quality guidelines from around the world into estimates of the proportion of crustacean taxa predicted to be impacted by exposure to a pesticide at the water quality guideline concentration. This analysis suggests 64% of long-term water quality guidelines and 88% of short-term pesticide water quality guidelines are not protective of the aquatic life they are designed to protect. We conclude that empirically derived data from mesocosm studies should be incorporated into water quality guideline derivation for pesticides where available. Also, interspecific differences in susceptibility should be accounted for more accurately to ensure water quality guidelines are adequately protective against the adverse effects of pesticide exposure.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".