Increasing the target-specificity of ERADICAT® for feral cat (Felis catus) control by encapsulating a toxicant
Bibliographic record
Abstract
ERADICAT®, a sausage-type meat bait, has been developed for use in managing feral cat (Felis catus) populations throughout Western Australia. However, concern about potential exposure of non-target species to bait-delivered toxicants has led to the development of a technique to more specifically target feral cats using a pellet. Research into the consumption, by cats and native animals, of toxic pellets implanted within the ERADICAT® bait has been simulated using ball bearings as a substitute pellet. Results from our work indicate that encapsulating the toxicant may pose less risk of poisoning to chuditch (Dasyurus geoffroii), woylies (Bettongia pencillata) and southern brown bandicoots (Isoodon obesulus) as they consumed significantly fewer ball bearings (P = 0.003, <0.001, <0.001) than semi-feral cats (P = 0.07). Theoretically, a toxic pellet will not reduce the effectiveness of the ERADICAT® bait as there was no significant difference between consumption of baits and the consumption of ball bearings in feral cats (P = 0.07). Therefore, baits containing a toxic pellet have the potential to be a more selective method to control feral cats.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".