An Evaluation of Feral Cat Management Options Using a Decision Analysis Network
Bibliographic record
Abstract
The feral domestic cat (Felis catus) is a predatory invasive species with documented negative effects on native wildlife.The issue of appropriate and acceptable feral cat management is a matter of contentious debate in cities and states across the United States due to concerns for wildlife conservation, cat welfare, and public health.Common management strategies include: Trap-Neuter-Release, Trap-Neuter-Release with removal of kittens for adoption and Trap-Euthanize.Very little empirical evidence exists relevant to the efficacy of alternative options and a model-based approach is needed to predict population response and extend calculations to impact on wildlife.We have created a structured decision support model representing multiple stakeholder groups to facilitate the coordinated management of feral cats.We used a probabilistic graphical model (a Bayesian Belief Network) to evaluate and rank alternative management decisions according to efficacy, societal preferences, and cost.Our model predicts that Trap-Neuter-Release strategies would be optimal management decisions for small local populations of less than fifty cats while Trap-Euthanize would be the optimal management decision for populations greater than 50 cats.Removal is predicted to reduce feral cat populations quickly and prevent cats from taking a large number of wildlife prey.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".