Original Articles Mosquito Vector Biting and Community Protection in a
Bibliographic record
Abstract
Background: Use of bed-net continues to offer potential strategy for malaria prevention in endemic areas. Local communities are indispensable during design and implementation stages. Methods: A cross-sectional study of 192 randomly selected inhabitants was carried out in malarious zone, Siahoo direstrict, Hormozgan Province, southern Iran. In addition, we monitored human landing periodicity of main malaria vectors and as well as self-protection of inhabitant in the study area for a period of one transmission season between April to October 2006. Results: The biting activities were seen throughout the whole night for three malaria vectors, Anopheles fluviatilis, An. stephensi and An. dthali, and An. fluviatilis exhibiting bimodal peaks, the first at midnight (0:00−1:00) and the other before dawn (5:00−6:00 am) but the maximum biting activity of An. stephensi was occurred at second quarter of night (11:00−12:00 pm). The majority of interviewers (83.3%) knew that malaria was transmitted by mosquitoes and 70.3 % of them stated that bed-net is the best control measures. Most subjects (62%) did not have a mosquito net. Conclusion: Study subjects were aware of an association between mosquito bite and malaria transmission. Health workers at different levels of the health care delivery system should disseminate relevant information about self-protection to help community members to be involved more in malaria control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".