Factors Contributing to Recent House Mouse Eradication Failures on Islands: An Initial Assessment Following a Workshop in New Zealand
Bibliographic record
Abstract
Invasive house mice threaten native biodiversity on many of the world’s islands. Best practice for eradicating house mouse populations from islands currently relies on bait containing the anticoagulant rodenticide brodifacoum. These baits are typically either broadcast (by hand or by helicopter in natural areas) or placed in bait stations (in human infrastructure or in areas where open broadcast is not permitted). There have been many successful mouse eradications using these methods, including 29 of 36 attempts of islands being successful (81%) in New Zealand. Following recent failed mouse eradications on Gough Island (South Atlantic, 2021) and Midway Atoll (North Pacific, 2023), a workshop was convened with 24 people attending (16 in-person, 8 on-line) from 7 countries (Australia, Canada, France, NZ, South Africa, UK, US), to discuss some hypotheses for what may have contributed to these unsuccessful outcomes. The workshop was held in Palmerston North, New Zealand, between November 27 and 29, 2023. Discussions over the three days revolved around three hypotheses. We present the key factors hypothesized for why eradications failed on these two islands. We also outline research and operational needs that were identified in the workshop that can contribute to improved outcomes for future eradications of house mice from targeted islands.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".