A dynamic population model for tsetse (Diptera: Glossinidae) area‐wide integrated pest management
Bibliographic record
Abstract
Abstract A spatial model of tsetse (Glossina palpalis ssp. and G. pallidipes) life cycle was created in FORTRAN, and four control measures [aerial spraying of non‐residual insecticides, traps and targets, insecticide‐treated livestock (ITL) and the sterile insect technique] were programmed into the model to assess how much of each of various combinations of these control tactics would be necessary to eradicate the population. The model included density‐independent and ‐dependent mortality rates, temperature‐dependent mortality, an age‐dependent mortality, two mechanisms of dispersal and a component of aggregation. Sensitivity analyses assessed the importance of various life history features and indicated that female fertility and factors affecting survivorship had the greatest impact on the equilibrium of the female population. The female equilibrium was likewise reduced when dispersal and aggregation were acting together. Sensitivity analyses showed that basic female survivorship, age‐dependent and temperature‐dependent survivorship of adults, teneral‐specific survivorship, daily female fertility, and mean temperature had the greatest effect on the four applied control measures. Time to eradication was reduced by initial knockdown of the population and due to the synergism of certain combinations of methods [e.g., traps‐targets and sterile insect technique (SIT); ITL and SIT]. Competitive ability of the sterile males was an important parameter when sterile to wild male overflooding ratios were small. An aggregated wild population reduced the efficiency of the SIT, but increased it with increased dispersal. The model can be used interactively to facilitate decision making during the planning and implementation of operational area‐wide integrated pest management programs against tsetse.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".