Microsatellite analysis of North American pine marten (<i>Martes americana</i>) populations from the Yukon and Northwest Territories
Bibliographic record
Abstract
Elucidating the population genetic structure of a species gives us insight into the levels of gene flow between geographic regions. Such data may have important implications for those trying to manage a heavily harvested wildlife species by determining the genetic connectivity of adjacent populations. In this study, the population structure of 12 North American pine marten (Martes americana) populations from the Yukon through to the central Northwest Territories was investigated using 11 microsatellite loci. Genetic variation within populations across the entire geographic range was relatively homogeneous as measured by: mean number of alleles (5.89 ± 0.45) and the average unbiased expected heterozygosity (He) (65.6 ± 1.7%). The overall unbiased probability of identity showed more variance between populations (1/10.25 ± 7.84 billion) than did the mean number of alleles and the Heestimates. Although some population structure was found among the populations, most regions were not strongly differentiated from one another. The low level of structure among the populations can, in part, be attributed to isolation by distance rather than to population fragmentation, as would be expected in more southerly regions in which suitable habitat is more disjunct. Furthermore, the low levels of population genetic structure were likely due to high levels of gene flow between regions and to large effective marten populations in the northern part of their distribution.
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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 | 0.000 | 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) | 0.000 | 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 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".