Distance from human settlements favors wild‐type appearance of feral cats (<i>Felis catus</i>) in Mediterranean woodland
Bibliographic record
Abstract
) were domesticated thousands of years ago, but despite artificial selection for a wide variety of coat colors, the wild-type pattern of tabby cats is very common. We aimed to determine whether this pattern grants an advantage over other morphs in natural environments. We collected cat images taken with camera traps in natural areas near and far from 38 rural settlements in Israel, to compare the habitat use by feral cats of different colors. We tested the effect of proximity to villages and habitat vegetation (normalized difference vegetation index, NDVI) on the probability of space use by the tabby morph compared to the others. NDVI had a positive effect on site use in both morphs, but non-tabby cats had a 2.1 higher probability of using the near sites than the far sites, independent of NDVI. The wild-type tabby cats' probability of site use were equally likely to be unaffected by proximity, or have an interaction of proximity with NDVI whereby the far transects are used with increasing probability in sites of denser vegetation. We hypothesize that the camouflage of tabby cats, more than other colors and patterns, confers an advantage in roaming the woodland habitats for which this pattern evolved. This has both theoretical implications as rare empirical evidence of the adaptive value of fur coloration, and practical implications on managing the ecological impact of feral cats worldwide.
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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 | 0.001 |
| 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) | 0.003 | 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".