Seasonal exposure of fish to neurotoxic pesticides in an intensive agricultural catchment, Uma-oya, Sri Lanka: Linking contamination and acetylcholinesterase inhibition
Bibliographic record
Abstract
The annual cultivation pattern in the Uma-oya catchment in Sri Lanka is characterized by Yala and Maha rainfall periods and associated cropping. Two cultivation seasons were compared for pesticide residues: base flow, field drainage, and the runoff and supplementary sediment data for three sites in the catchment. Organophosphate and N-methyl carbamate pesticide analysis confirmed a higher concentration in the Yala season with low-flow conditions. Acetylcholinesterase (AChE) activity was measured by standard spectrometry in the brain, muscle, and eye tissues of three freshwater cyprinid fishes, Garra ceylonensis, Devario malabaricus, and Rasbora daniconius from three study sites during months overlapping two seasons in 2010 (December) and 2011 (July). Baseline AChE data were measured from fish samples from a forested reserve in the Knuckles. A 73% inhibition in muscle AChE activity in G. ceylonensis was associated with intense pesticide exposure months in the Yala season. The AChE inhibition more than 70% in G. ceylonensis eyes in both Yala (76%) and Maha (72.5%) seasons indicates particular sensitivity of eye tissue to inhibitors. The less dramatic AChE inhibition in the eye tissues in D. malabaricus and R. daniconius in both seasons indicates exemplary protective capacity of muscle AChE in fish. The highest inhibition of AChE (up to 60% in brain and up to 56% in muscle AChE activity in R. daniconius and up to 47.8% in brain and up to 64.6% in muscle AChE activity in D. malabaricus) occurred during the Yala season. Tissue AChE activity and physiological activity in fish were correlated. The results collectively indicate that AChE is a consistent biomarker for diffused contaminant exposure in agricultural catchments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".