Dichlorodiphenyltrichloroethane in the aquatic ecosystem of the Okavango Delta, Botswana, South Africa
Bibliographic record
Abstract
Concentrations of DDT and its metabolites were measured in water, plants, invertebrates, and fish from lagoons in the Okavango Delta, Botswana (Africa), where DDT has been used for approximately 50 years. The sampling area was sectioned to distinguish spraying for malaria and for African sleeping sickness. Average concentrations of total DDT (sum of DDT and its metabolites) in the Okavango ranged from 0.009 ng/L in water to 18.76 ng/g wet weight in fish. These levels are approximately 1% of those found in piscivorous fish from temperate North America. The dichlorodiphenyl ethylene (DDE) metabolite was the most abundant fraction of total DDT. Although total DDT concentrations were higher in areas treated for malaria than areas treated for sleeping sickness, these concentrations were likely driven by factors other than the historic application of the pesticide. Equilibration with air concentrations is the most likely explanation for these levels. Since the mean annual temperature exceeds the temperature of vaporization of DDT, this research points to the need for reliable transport models. Our results showed that total DDT concentration in fish was best explained by lipid content of the fish and trophic position inferred by delta15N, regardless of DDT application history in those areas. The reservoir above Gaborone Dam, an area downstream of the Okavango but where DDT had not been used, was sampled to compare total DDT levels to the treated areas. The two species (a herbivorous threespot talapia and the omnivorous sharptooth catfish) from Gaborone had levels higher than those found in the Okavango Delta, but these differences can again be explained using trophic position inferred by delta15N rather than by fish size or location.
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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 | 0.001 | 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".