Genetic distinction of wildcat (<i>Felis silvestris</i>) populations in Europe, and hybridization with domestic cats in Hungary
Why this work is in the frame
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Bibliographic record
Abstract
The genetic integrity and evolutionary persistence of declining wildcat populations are threatened by crossbreeding with widespread free-living domestic cats. Here we use allelic variation at 12 microsatellite loci to describe genetic variation in 336 cats sampled from nine European countries. Cats were identified as European wildcats (Felis silvestris silvestris), Sardinian wildcats (F. s. libyca) and domestic cats (F. s. catus), according to phenotypic traits, geographical locations and independently of any genetic information. Genetic variability was significantly partitioned among taxonomic groups (FST = 0.11; RST = 0.41; P < 0.001) and sampling locations (FST = 0.07; RST = 0.06; P < 0.001), suggesting that wild and domestic cats are subdivided into distinct gene pools in Europe. Multivariate and Bayesian clustering of individual genotypes also showed evidence of distinct cat groups, congruent with current taxonomy, and suggesting geographical population structuring. Admixture analyses identified cryptic hybrids among wildcats in Portugal, Italy and Bulgaria, and evidenced instances of extensive hybridization between wild and domestic cats sampled in Hungary. Cats in Hungary include a composite assemblage of variable phenotypes and genotypes, which, as previously documented in Scotland, might originate from long lasting hybridization and introgression. A number of historical, demographic and ecological conditions can lead to extensive crossbreeding between wild and domestic cats, thus threatening the genetic integrity of wildcat populations in Europe.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it