10. The influence Kingston feral cat populations have on local biodiversity
Bibliographic record
Abstract
Focus: Kingston Feral Cat (Felis catus) PopulationLocation: Greater Kingston AreaIssue: Released domesticated cats have established feral populations within the Kingston area. Predation by the feral cats has caused damage to the local bird and small mammal populations. The cats also displace other mammals that have previously occupied the same ecological niche. Furthermore, the increased cat population has led to an increase in the flea and disease count. Overall they decrease biodiversity in the Kingston area. Objective: We aim to promote and inform the general public of this occurrence, so that the communitycan be involved with helping manage and decrease the damaging effects of the feral population. Methods:1. Survey a local colony of feral cats to further understand the extent of the problem.2. Work with local conservation initiatives (ex. Kingston Spay and Neuter Initiative) to gain hands onexperience in dealing with the feral cats.3. To raise awareness by utilizing social media (creating images people want to share on Facebook,Twitter, Tumblr etc.)Ultimate goal: With this project we hope to alter the problem of feral cats with a two-fold solution. By increasing public awareness we hope to decrease the release of domesticated cats into the wild, promote owners spaying and neutering their pets and encourage others to perform further work. We hope to utilize our own skills and gain experience by working on the ground with local initiatives to learn the best methods to tackle this issue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".