Ecological Risk Assessment of Agricultural Pesticides throughout the Shadegan Wetland, Iran
Bibliographic record
Abstract
As a major ecosystem type, wetland provides invaluable ecological services. Environmental pollution, especially pesticide runoffs should be paid more attention to keep wetlands healthy. This paper proposes two methodologies namely, deterministic and probabilistic approaches. Deterministic approach or risk quotient (RQ) was calculated using the water concentration and toxicant reference values of five pesticides (DDT, Aldrine, Dieldrin, Ametryn, Lindane). For probabilistic approach, hazardous concentrations for 5% of species (HC5) were estimated. The results of deterministic approach showed that the RQ for shirbot or large scaled barb (Barbus grypus), benni (Barbus sharpeyi), golden barb (Barbus luteus) and insect larvae (Chironomus sp.) is high and the environment is exposed to higher risk. However, the results of probabilistic approach and HC5 showed that DDT and Lindane are the most harmful pesticides and can create unsuitable environment. It is recommended that proper countermeasures should be implemented to reduce the risks.
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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 | 0.000 | 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) | 0.000 | 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 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".