Characteristics of<i>Anopheles arabiensis</i>larval habitats in Tubu village, Botswana
Bibliographic record
Abstract
Documented information on the ecology of larval habitats in Botswana is lacking but is critical for larval control programs. Therefore, this study determined the characteristics of these habitats and the influences of biotic and abiotic factors in Tubu village, Botswana. Eight water bodies were sampled between January and December, 2013. The aquatic vegetation and invertebrate species present were characterized. Water parameters measured were turbidity (NTU), conductivity (μS/cm), oxygen (mg/l), and pH. Larval densities of Anopheles arabiensis mosquitoes and their correlation with abiotic factors were determined. Larval breeding was associated with 'short' aquatic vegetation, a variety of habitats fed by both rainfall and flood waters and sites with predators and competitors. The monthly mean (± SE(mean)) larval density was 8.16±1.33. The monthly mean (±SE(mean)) pH, conductivity, oxygen, and turbidity were 7.65±0.13, 1152.834±69.171, 5.59±1.33, and 323.421±33.801, respectively. There was a significant negative correlation between larval density and conductivity (r = -0.839; p < 0.01), while a significant positive correlation occurred between turbidity and larval density (r = 0.685; p < 0.05). Oxygen (r = 0.140; p > 0.05) and pH (r = 0.252; p > 0.05) were not correlated with larval density. Floods and diversified breeding sites contributed to prolonged and prolific larval breeding. 'Short' aquatic vegetation and predator-infested waters offered suitable environments for larval breeding. Turbidity and conductivity were good indicators for potential breeding places and can be used as early warning indices for predicting larval production levels.
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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) | 0.001 | 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".