Comparison of various configurations of CDC-type traps for the collection of Phlebotomus papatasi Scopoli in southern Israel
Bibliographic record
Abstract
We conducted two experiments to determine the best CDC-trap configuration for catching male and female Phlebotomus papatasi. First, visual features were evaluated. Standard CDC traps were modified to have black or white catch bags, black or white lids, or no lids and these were tried in different combinations. Significantly more male sand flies were caught by darker traps; significantly more females were captured by traps with either all black or a combination of black and white features. Attraction may be due to dark color or contrast in colors. CDC traps with suction and the following features were also evaluated: no light; incandescent light; ultraviolet (UV) light; combination of black color, heat and moisture; CO(2) alone, or a combination of black color, heat, moisture, and CO(2) simultaneously, all in upright and inverted positions, with the opening for insect entry always 50 cm above the ground. Significantly more females than males were caught by all traps (standard and inverted) except the control traps with suction only. Traps with CO(2) caught more sand flies than traps without CO(2.) Traps with black color, heat and moisture captured significantly more sand flies than the control traps, but with the addition of CO(2) , these traps catch significantly more sand flies than the other traps evaluated. Inverting traps increased the catch for like traps by about two times.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".