Extensive field-sampling reveals the uniqueness of a trophy mountain goat population
Bibliographic record
Abstract
ABSTRACT Collaborations between academic researchers and agencies is crucial for genetic data to have a tangible impact on conservation and wildlife management. Such partnerships are particularly important elusive species where the difficult terrain requires that a significant amount of resources and a combination of methods be used to estimate population parameters needed for conservation. We report and multi-year academic-agency collaboration on the North American mountain goat that used an extensive field sampling of genetic and phenotypic data to determine whether, and to what degree, genetic and phenotypic differences separate an isolated population of mountain goats on the Cleveland Peninsula form those in southeast Alaska. We observed significantly larger horns on the peninsula and the population appears demographically isolated. Isolation-by-distance accompanied by limited migration and low effective population size on the Cleveland Peninsula suggest this population will continue to lose genetic diversity. While the large horns of mountain goats have generated interest in re-opening mountain goat harvest on Cleveland Peninsula, our genetic data suggest this population is vulnerable to demographic and environmental perturbations and is unlikely to support a sustained harvest.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".