Different methods of using attractive sugar baits (ATSB) for the control of Phlebotomus papatasi
Bibliographic record
Abstract
We have previously shown that fermented ripe fruit is a strong attractant for several mosquito species, and when mixed with oral insecticide these attractive toxic sugar baits (ATSB) were highly effective for local mosquito control. In the present study, we compared the effects of ATSB presented in different ways on isolated populations of Phlebotomus papatasi Scopoli. Experiments were carried out in the arid habitat of the Jordan valley, Israel where the effectiveness of three methods was compared: ATSB sprayed on patches of vegetation, net fence coated with ATSB, and bait stations soaked with ATSB. Spraying ATSB reduced the population to about 5% of the control area population. Barrier ATSB coated fences, had a similar effect decreasing the population to about 12% of the concurrent catch in the control site. The effect of ATSB presented on bait stations was much smaller and compared to the control, only caused the population to be reduced to 40%. In the control areas where only food dye marker was used, the solution presented on bait stations only marked an average of 22.3% of female sand flies while spraying vegetation and using barrier fences in the two other experiments marked about 60% of the females. Our experiments show that ATSB either sprayed on the vegetation or on barrier fences is an effective means against sand flies at least in arid areas where attractive plants are scarce or absent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".