Microsatellites reveal clear genetic boundaries among Atlantic salmon (<i>Salmo salar</i>) populations from the Barents and White seas, northwest Russia
Bibliographic record
Abstract
Fourteen microsatellite loci were employed to study the genetic structure of 34 Atlantic salmon ( Salmo salar ) populations from the White and Barents seas area, the last major European region where the species has remained in its natural state. The populations were separated into four distinct clusters (Atlantic Ocean and western Barents Sea, Kola Peninsula, western White Sea, and eastern Barents Sea) within which genetic divergence varied between 0.02 and 0.10 as estimated with FST. When this structuring was contrasted with previously identified mtDNA-based groupings, a remarkable similarity was observed, implying that these four groups can be considered as a good starting point for defining management units in the region. Indeed, several approaches for assessing every population’s conservation value suggested that conservation of populations from each observed cluster would maximize preservation of the region’s genetic diversity. Furthermore, each unit may require differing management strategies, as distinct patterns of genetic diversity and divergence characteristics were detected. In addition, individual assignment success within a region was high (87%–96%), indicating that the data can be used as a baseline to differentiate individuals caught in offshore fisheries on a regional level with a relatively high degree of accuracy.
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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 | 0.000 |
| 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".