Temporal dynamics of genetic variability in a mountain goat (Oreamnos americanus) population
Bibliographic record
Abstract
The association between population dynamics and genetic variability is of fundamental importance for both evolutionary and conservation biology. We combined long-term population monitoring and molecular genetic data from 123 offspring and their parents at 28 microsatellite loci to investigate changes in genetic diversity over 14 cohorts in a small and relatively isolated population of mountain goats (Oreamnos americanus) during a period of demographic increase. Offspring heterozygosity decreased while parental genetic similarity and inbreeding coefficients (F(IS) ) increased over the study period (1995-2008). Immigrants introduced three novel alleles into the population and matings between residents and immigrants produced more heterozygous offspring than local crosses, suggesting that immigration can increase population genetic variability. The population experienced genetic drift over the study period, reflected by a reduced allelic richness over time and an 'isolation-by-time' pattern of genetic structure. The temporal decline of individual genetic diversity despite increasing population size probably resulted from a combination of genetic drift due to small effective population size, inbreeding and insufficient counterbalancing by immigration. This study highlights the importance of long-term genetic monitoring to understand how demographic processes influence temporal changes of genetic diversity in long-lived organisms.
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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 | 0.000 | 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".