Monitoring pesticides residues in water resources of the Lake Naivasha catchment using passive sampling
Bibliographic record
Abstract
Passive samplers are able to recover the detection of low concentrations using continuous gathering the pollutants.While these contaminants might be undetected with the conventional grab sampling which is an once-off time-point sampling.In this study, Silicone rubber sheet samplers were used as the passive sampler to monitor the residue of some organochlorine pesticides, notably; α-HCH, β-HCH, γ-HCH, δ-HCH, Heptachlor, Aldrin, Heptachlor Epoxide, pp-DDE, Endrin, Dieldrin, α-endosulfan, β-endosulfan, pp-DDD, Endrin aldehyde, pp-DDT, Endosulfan Sulphate and Methoxychlor in the Lake Naivasha basin, Kenya.The samplers were deployed in the water for one month after which the concentration of the pesticides was measured by analyzing the extraction of samplers using the GC-ECD.Determining the organochlorine pesticides residues by means of the Silicone rubber samplers demonstrated that the maximum contamination occurred at the lake site with the total sum concentration of 81ng/L which is the final accumulation location for surficial hydrological, chemical and sediment transport through the river basin.The total organochlorine residue changed to 71.5ng/L for the Middle Malewa and 59ng/L for the Upper Malewa river sampling sites.Finally, comparing the concentration of the studied pesticides with the maximum standard limit showed that the concentrations were below the limit.However, because of the risk of these pesticides continued monitoring of pesticides residues in the catchment remains highly recommended.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".