Genetic population structure of broad whitefish, Coregonus nasus, from the Mackenzie River, Northwest Territories: implications for subsistence fishery management
Bibliographic record
Abstract
We assayed microsatellite DNA variation among 1013 broad whitefish, Coregonus nasus , from 36 localities within the lower Mackenzie River (Northwest Territories, Canada) to provide the first assessment of fine-scale population structuring of broad whitefish in this large system. Among sampling locations, averaged across all loci, the number of alleles ranged from 3.00 to 6.71 and heterozygosity averaged 0.54. Population subdivision was generally low, but significant (θ = 0.026, P < 0.05), although pairwise comparisons indicated that overall significance was heavily influenced by comparisons between anadromous and lacustrine groups. Bayesian-based STRUCTURE analysis suggested that there are two main genetic groups within our study area: anadromous and lacustrine broad whitefish. A mixture analysis indicated that all populations contribute to the lower Mackenzie River subsistence fishery, yet catches were dominated by Peel River fish, highlighting the importance of this tributary. Our data also supported the idea that there are several units of conservation among Mackenzie River system broad whitefish populations and that management strategies should be implemented accordingly.
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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 | 0.001 |
| 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".