An accessible scheme for monitoring free‐roaming cat population trends
Bibliographic record
Abstract
Abstract Free‐roaming cats (FRCs) form nondomiciliary population groups that might lead to adverse environmental effects, as well as to welfare impairment of the cats themselves. Though criticized by ecologists, for the last two decades, the trap–neuter–return (TNR) programs were often employed aiming to manage these populations. At present, no accepted and accessible monitoring scheme exists to determine the effectiveness of those programs. In the current study, we present the reliability and validity of an applicable monitoring scheme, as an adjunct tool for a TNR program of FRC in an urban environment. The monitoring scheme is based on cat observation counts along randomly chosen transects. Fifty‐four transects were repeatedly walked for three years, between 2012‐2014, in 27 neighborhoods within an urban area of 19.3 Km2. Cat numbers counted in the 2014 observations were significantly higher than cat numbers found in the 2012 observations (prevalence ratio = 1.258, CI95%= 1.198–1.322, p < 0.001). The method revealed high reliability when different observers and different transects in the same neighborhood were compared (R2 = 0.548 and R2 = 0.391, respectively, for measuring cat counts per km, p < 0.001; and R2 = 0.5 and R2 = 0.74, respectively, for measuring neutering percentage, p < 0.001). This scheme was constructively validated by measurements of municipal data on the number of neutered cats and demonstrated high correlation (R2 = 0.59, p < 0.001). Conducting cat observations using friendly calling and feeding resulted in an increased number of FRC observed per km walk (by 79% and 22%–30%, respectively). However, these manipulations did not alter the recorded percentage of neutered cats. The proposed scheme provides spatio‐temporal data that can contribute to the management programs of such cat metapopulations in an urban environment.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".