REDUCED GENETIC DIVERSITY IN TWO INTRODUCED AND ISOLATED MOOSE POPULATIONS IN ALASKA
Bibliographic record
Abstract
I examined indices of genetic diversity in 2 isolated moose (Alces alces) populations in Alaska that were founded by low numbers of individuals to determine effects of founding and infer whether subsequent gene flow has occurred with surrounding moose populations. Kalgin Island is a small, predator-free island in Cook Inlet that was founded by 6 moose (3 females) in the late 1950s; its population has since undergone dramatic fluctuations. Berners Bay is an isolated population along the coast of southeastern Alaska that was founded by 21 calves introduced in 1958-1960. Genetic attributes of those populations were compared to a population in Yukon Flats in central Alaska that served as an outbred control. Indices from 11 microsatellite markers indicated substantial effects of founding and subsequent isolation. Heterozygosity and allelic diversity, both of which are reduced by genetic bottlenecks, were significantly lower in the introduced populations than the Yukon Flats population. Kalgin Island diversity was significantly lower than that for Berners Bay, and was likely due to the smaller founding size and subsequent population fluctuations. Neither introduced population exhibited evidence of gene flow from surrounding populations. Managers should consider the isolation of those populations when assessing risks to population viability and crafting management strategies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".