Genetic connectivity of wolverines in western North America
Bibliographic record
Abstract
Abstract Wolverine distribution contracted along the southern periphery of its range in North America during the 19th and 20th centuries due primarily to human influences. This history, along with low densities, sensitivity to climate change, and concerns about connectivity among fragmented habitats spurred the recent US federal listing of threatened status and special concern status in Canada. To help inform large scale landscape connectivity, we collected 882 genetic samples genotyped at 19 microsatellite loci. We employed multiple statistical models to assess the landscape factors (terrain complexity, human disturbance, forest configuration, and climate) associated with wolverine genetic connectivity across 2.2 million km2 of southwestern Canada and the northwestern contiguous United States. Genetic similarity (positive spatial autocorrelation) of wolverines was detected up to 555 km and a high-to-low gradient of genetic diversity occurred from north-to-south. Landscape genetics analyses confirmed that wolverine genetic connectivity has been negatively influenced by human disturbance at broad scales and positively influenced by forest cover and snow persistence at fine- and broad–scales, respectively. This information applied across large landscapes can be used to guide management actions with the goal of maintaining or restoring population connectivity.
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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 | 0.001 |
| 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) | 0.001 | 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".