Risk Posed by Pesticides to Aquatic Organisms in Rivers of Northern Inland New South Wales, Australia
Bibliographic record
Abstract
An ecological assessment was conducted to determine the risk posed by agricultural pesticides to inland rivers of north-west New South Wales (NSW), Australia. A preliminary screening of 30 pesticides provided a short-list of eight for further investigation (atrazine, chlorpyrifos, diuron, endosulfan, fluometuron, metolachlor, profenofos, prometryn). Selection was based on chemical characteristics, toxicity, detection frequencies and environmental concentrations. Hazard quotients were calculated for both spray and non-spray seasons. Where possible, hazard quotients were calculated for both acute and chronic exposures for crustaceans, insects, micro-organisms, molluscs, plants and vertebrates. Chlorpyrifos, endosulfan and profenofos posed a high hazard (HQ > 0.5). A probabilistic risk analysis indicated that chlorpyrifos, endosulfan and profenofos posed a risk from acute exposure during the spray season, while endosulfan also posed a risk from chronic exposure during the spray season. The risks posed by profenofos and chlorpyrifos were characterised by a low probability of detection, but these detections affected a high percentage of species. The risks posed by acute and chronic exposures of endosulfan were characterised by a high probability of detection, but only a limited number of these detections affected a high percentage of species. Risk during the non-spray season was not assessed, as the detection of pesticides was infrequent during this period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".