Origin and genetic structure of a recovering bobcat (<i>Lynx</i> <i>rufus</i>) population
Bibliographic record
Abstract
Genetic analyses can provide important insights into the demographic processes that underlie recovering populations of mammals of conservation concern such as felid species. To better understand the recent and rapid recovery of bobcats (Lynx rufus (Schreber, 1777)) in Ohio, we analyzed samples from four states in the lower Great Lakes Region using 12 microsatellite DNA loci and a portion of the mtDNA control region. Our results showed that a newly established population of bobcats in the eastern part of Ohio was genetically distinct from a multistate population distributed across Kentucky, southern Ohio, West Virginia, and western Pennsylvania. There was no direct genetic evidence of a bottleneck or inbreeding in this population. A lack of private alleles and only slightly lower levels of allelic richness and heterozygosity compared with its neighbors suggest that the eastern Ohio population likely originated from the migration of relatively large numbers of individuals from a source population rather than re-emerging from an undetected residual population. We recommend that a management plan should define the areas occupied by the two populations in Ohio as separate management units at least for the near future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".