Genetic variation and population structure of moose (<i>Alces alces</i>) at neutral and functional DNA loci
Bibliographic record
Abstract
Genetic variation was examined for moose (Alces alces) from Riding Mountain, Isle Royale, and Pukaskwa national parks; northwestern, Nipigon, northeastern, and central Ontario; New Brunswick; and Newfoundland. The national parks were identified as maintaining potentially different local selection pressures due to the absence of hunting and the presence or absence of the parasite Parelaphostrongylus tenuis. Genetic variation was estimated using neutral DNA markers, assessed by multilocus DNA fingerprinting and five microsatellite loci, and the functional antigen binding region (ARS) (exon 2) of the major histocompatibility complex (MHC) gene DRB. There was discordance in the allelic diversity observed at the neutral loci compared with the MHC DRB locus in a number of populations. Ontario populations demonstrated higher levels of variability at the neutral loci and relatively low levels at the DRB locus. Conversely, the Isle Royale population has the lowest genetic variability, consistent with a historic small founding event, at the neutral DNA markers and relatively high variability at the MHC gene. Relatively high levels of genetic variation at the DRB locus were observed in protected park populations concomitant with the absence of white-tailed deer (Odocoileus virginianus) or the parasite P. tenuis and an absence of hunting. Gene flow was observed among the neighboring geographic regions within Ontario, including Pukaskwa National Park, with evidence of isolation-by-distance among more distant regions within Ontario. The discordant patterns between DNA markers suggest that neutral DNA markers may not accurately reflect adaptive variation present at functional loci.
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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) | 0.000 | 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".