Measuring Oviposition Preference in<i>Aedes aegypti</i>Mosquitoes
Bibliographic record
Abstract
The female mosquito is endowed with the responsibility of gene transfer from one generation to another through careful delivery of her eggs to an appropriate environment. To guarantee a successful reproduction process, female mosquitoes use an evaluative approach to select an oviposition site that will ensure that their progeny successfully hatch and develop from larvae to pupa to adult. Specific conditions must be met during oviposition site selection before gravid females deposit eggs, including but not limited to appropriate temperature, salinity, and pH; the presence of nutrition; and a low risk of predation. Mosquito species exhibit a remarkable diversity in their oviposition site selection behaviors. For instance,Aedes aegypti, which is a vector for Zika, dengue, yellow fever, and chikungunya viruses, prefers to lay its eggs in natural depressions (e.g., tree holes) or artificial containers. In contrast, other mosquito species, likeAnopheles, gravitate toward more open water bodies. Understanding the oviposition preference behavior of theAe. aegyptimosquito will facilitate the development of surveillance and control efforts to hinder the successful progression of this vector. Here, we present information on some of the known behaviors and preferences ofAe. aegyptiduring oviposition site selection, techniques to measure these preferences, and some open questions.
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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 | 0.000 |
| 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".