Conclusions from a dynamic population model for tsetse: response to comments
Bibliographic record
Abstract
Abstract The critique by Hargrove et al. (Popul Ecol, ) of our recently published paper on a tsetse population model (Barclay and Vreysen in Popul Ecol 53:89–110, ) has made some good points but has also misinterpreted the intent of some of our results as we presented them. Hargrove et al. rightly say that there is a mismatch between the size of the unit cells in the model (1 ha) and the iteration rate of the model (every 5 days), yielding too low a dispersal rate to simulate reality. However, they have misconstrued several of our results that we presented as examples to imply that those results were a necessary condition for control of tsetse, especially using traps and targets.
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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 | 0.021 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.028 | 0.042 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".