Population subdivision and genetic signatures of demographic changes in Arctic grayling (<i>Thymallus arcticus</i>) from an impounded watershed
Bibliographic record
Abstract
We examined allelic variation at seven microsatellite loci in 11 samples of Arctic grayling (Thymallus arcticus) from the Peace River watershed, northeastern British Columbia, to (i) assess population subdivision and (ii) test for population size changes promoted by hydroelectric dam construction in the watershed. The number of alleles and expected heterozygosity per locus averaged 2.3 and 0.29, respectively. Overall Fst(θ) was 0.21 (P < 0.003), but there was no distinction between age classes (0+ and 3+) within two streams (θ = 0.01, P > 0.05). Seven percent of the microsatellite variation (P < 0.005) was attributable to differences between samples above and below a historical natural barrier to upstream fish migration, the Peace River Canyon (the site of hydroelectric developments since the 1960s). Strong isolation-by-distance among samples was resolved (Mantel r = 0.64, P < 0.01). Coalescent analyses suggested that current Arctic grayling population sizes are less than 1% of historical sizes and that this decline began relatively recently (i.e., <300 years ago) under an exponential model of population size change or earlier in the late Pleistocene under a linear model. Significant microsatellite divergence occurs among Peace River Arctic grayling populations previously characterized by low mtDNA divergence.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".