The interaction of dispersal and control methods for the riverine tsetse fly <i>Glossina</i> <i>palpalis</i> <i>gambiensis</i> (Diptera: Glossinidae): a modelling study
Bibliographic record
Abstract
Abstract We used a spatial model of a riverine tsetse fly species Glossina palpalis gambiensis life cycle to investigate the interaction between their dispersal and three control methods and to document these interactions using sensitivity analyses. The model is currently limited to gallery forest habitat inhabited by Glossina palpalis gambiensis in the dry season in the sub‐humid zone of West Africa. The control methods modelled were traps and targets (TT), insecticide‐treated livestock (ITL), and the sterile insect technique (SIT). Both distance dispersed (up to 800 m) and percent of flies dispersing each day (up to 60 %) increased the efficiency of control by TT. Most of this increase occurred for low values of both distance dispersed and percent dispersing, but the increase continued up to the limits tried. The daily movement of cattle assisted the control program and when movement was considerable (up to 600 m daily) the effects were greater than the effects of tsetse dispersal. Random dispersal decreased aggregation and equilibrium population size, and thus also increased the efficiency of SIT. Dispersal that was mostly oriented towards clumps was of much less value for SIT but acted on TT and ITL similarly to random dispersal.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| 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".