Copula Modeling of Differential Effect of Leaf Species on Aedes albopictus Development Time
Bibliographic record
Abstract
Decaying leaves provide a major carbon source for mosquito larvae and leaf litter of different plant species vary in their ability to support mosquito growth, survival, and development. Thus analyzing the effects of leaf species treatment on development time of Aedes albopictus has the potential to discover a plant-based strategy for mosquito control. Here, we employ a statistical model named copula that provides a convenient methodology for modeling multivariate dependence to determine the association between leaf litter identify and mosquito performance. A copula that best fits the association of leaf litters on mosquito performance is selected, and statistical tests are performed to check the adequacy of the copula chosen. By computer-based Monte Carlo methods, a large number of simulated development times are generated under the copula chosen. From the simulated development times, we calculate the percentiles to determine expected development time of female Aedes albopictus under the five different leaf species treatments, and compared the results to those when all the effects of leaf infusion are combined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 teacher head, 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".