Assessment of Organophosphorus Pesticide Residues in Water and Sediment Collected from the Southern Caspian Sea
Bibliographic record
Abstract
Pollution of water resources by uncontrolled pesticide use is a serious health and environmental issue. In this study, concentrations of three organophosphorus pesticides (diazinon, malathion, and azinphos-methyl) in water and sediment samples from five estuaries (Sefidrud, Chalus, Babolrud, Tajan, and Gorganrud) along the Caspian Sea were investigated. Samples were collected from surface water and sediment during summer to autumn, and pesticides were analysed by gas chromatography-mass spectrometry (GC-MS). Results indicated that salinity and turbidity in Gorganrud were higher (salinity: range 2–8%; turbidity: range 1–9%) compared to other stations. Higher diazinon (water: 0.08±0.06, sediment: 0.04±0.01), malathion (water: 0.09±0.06, sediment: 0.05±0.01) and azinphos-methyl (water: 0.1±0.08, sediment: 0.06± 0.02) concentrations were observed in the Tajan river compared to other stations. Mean concentrations of diazinon, malathion and azinphos-methyl pesticides were higher in the summer compared to the autumn. Azinphos-methyl concentrations were higher than sediment quality guidelines (SQGs), which warrants ongoing monitoring. Our research provides insights into the presence of organophosphate pesticides (OPs) in rivers that enter into the Caspian Sea. Further work to better understand the environmental pollution of OPs in the Caspian Sea is ongoing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".