In‐stream population structuring of Lake Sturgeon in Northern Manitoba, Canada
Bibliographic record
Abstract
Abstract The Lake Sturgeon is a long‐lived, late‐maturing fish that declined significantly in abundance over the past 150 years. Since the 1990s, stocking has been used to recover numerous Lake Sturgeon populations across North America. Ill‐informed genetic mixing among populations can have unintended negative consequences, so a genotype‐by‐sequencing (GBS) study was undertaken to help guide the stocking strategy for Lake Sturgeon on the 653‐km‐long Nelson River, Manitoba. Tissue samples collected from 416 adults captured from 12 locations along the Nelson River, and from the Hayes and Churchill rivers that also empty into Hudson Bay, were sequenced using Illumina technology. A bioinformatics pipeline yielded 5637 high‐quality filtered markers. Genetic differentiation (overall meanFSTof 0.028; a range of means: 0–0.16) revealed spatial structuring among and within rivers. Two populations were found in the upper Nelson River, two more in the middle Nelson, and one in the lower Nelson. Discriminant analysis of principal components revealed first‐generation migrants and a general lack of effective dispersal, which raises questions about historical versus contemporary influence. Lake Sturgeon stocking in northern Manitoba should avoid mixing among rivers and among Nelson River sections.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".