Malaria vectors in an irrigated and in a rain-fed division of southern Sri Lanka
Bibliographic record
Abstract
Anopheles species composition and relative seasonal abundance were measured in an irrigated division (low historical malaria incidence) and in a rain-fed division (high historical malaria incidence) of southern Sri Lanka. Twelve species of anophelines were represented in adult and larval collections with Anopheles vagus Donitz being the most abundant. In cattle-baited net trap collections, Anopheles adults were significantly more abundant in the irrigated division than in the rain-fed division. In pyrethrum-spray sheet collections, cattle-baited but trap collections and larval collections, Anopheles abundance was significantly greater in the rain-fed division. Houses were of poorer construction in the rain-fed division, where pyrethrum-spray sheet collections consisted mainly of Anopheles subpictus Grassi (98%) and Anopheles culicifacies Giles (2%). Hut trap collections also consisted mainly of An. subpictus (88%) and An. culicifacies (7%). Net trap collections consisted mainly of An. vagus (43%) and Anopheles peditaeniatus Leicester (31%). Larval collections also consisted of An. peditaeniatus (24%) and An. vagus (21%). Weak associations were found between species abundance and environmental factors explored in this study (e.g., vegetation, water quality, sunlight exposure). The greater malaria risk in the rain-fed division is due in part to the occurrence of potential vectors in relatively higher numbers.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".