Microgeographic population structure of brook charr: a comparison of microsatellite and mark‐recapture data
Bibliographic record
Abstract
Polymorphism at five microsatellite genetic markers (genotyped n = 496) and mark‐recapture tagging data (tagged n = 9813) were used to define the population structure of brook charr, Salvelinus fontinalis from the Indian Bay watershed, Newfoundland, Canada. Despite the absence of physical barriers to migration among lakes, both genetic and tagging data suggest that brook charr in each lake represent reproductively isolated populations. Exact tests comparing allele frequencies, θ (global value = 0·063), Rst (global value = 0·052), individual assignment tests, and Nei's genetic distance provided congruent estimates of population subdivision in agreement with the tagging data (only 2·2% of recaptures were lake‐to‐lake). The genetic structure of the brook charr populations corresponded with the geographic structure of the drainage basin on a qualitative level, although linear distance over water was not significantly correlated with the tagging data or the genetic distance measures. The agreement between the tagging and the genetic data suggest that microsatellite markers can be useful tools for defining real biological units. The results also suggest that brook charr exhibit microgeographic population structure at the watershed scale, and that this is the scale at which conservation and management of this salmonid might best be implemented.
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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 | 0.001 |
| 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".