Modelling optimal timing and frequency of insecticide sprays for knockdown in preparation for other control measures
Bibliographic record
Abstract
A modelling investigation was conducted into optimizing the number of sprays and inter-spray interval to reduce an insect population to a low level, for example, prior to pheromone trapping or the release of sterile males. The model population was age-structured and density-dependent. If spray mortality is 100% for each spray, then the ideal spraying schedule is easily determined from the durations of the various life stages. For spray mortality of less than 100%, a simulation was used to determine optimal spraying schedules. Relative length of the larval period, fertility rate and age to first oviposition were found to be the most important biotic parameters for this determination. Their importance is magnified as spray mortality decreases. The stage targeted by sprays and the percent mortality caused by each spray are also important in determining the required number of sprays. Using medfly (Ceratitis capitata Wiedmann) biotic parameters as an example when the spray targets adults, it appeared that neither the stage at which density-dependent mortality takes effect, nor the form of the adult survivorship curve are important in determining the optimal spray schedule.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".