Can outbreaks of house mice in south-eastern Australia be predicted by weather models?
Bibliographic record
Abstract
Outbreaks of house mice (Mus domesticus) occur irregularly in the wheat-growing areas of south-eastern Australia, and are thought to be driven by weather variability, particularly rainfall. If rainfall drives grass and seed production, and vegetation production drives mouse dynamics, we should achieve better predictability of mouse outbreaks by the use of plant-production data. On a broader scale, if climatic variability is affected by El Niño–Southern Oscillation (ENSO) events, large-scale weather variables might be associated with mouse outbreaks. We could not find any association of mouse outbreaks over the last century with any ENSO measurements or other large-scale weather variables, indicating that the causal change linking mouse numbers with weather variation is more complex than is commonly assumed. For the 1960–2002 period we were only partly successful in using variation in cereal production to predict outbreaks of mice in nine areas of Victoria and South Australia, and we got better predictability of outbreaks from rainfall data alone. We achieved 70% correct predictions for a qualitative model using rainfall and 58% for a quantitative model using rainfall and spring mouse numbers. Without the detailed specific mechanisms underlying mouse population dynamics, we may not be able to improve on these simple models that link rainfall to mouse outbreaks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".