“Riverscape” genetics: river characteristics influence the genetic structure and diversity of anadromous and freshwater Atlantic salmon (<i>Salmo salar</i>) populations in northwest Russia
Bibliographic record
Abstract
Combining population genetic and landscape ecology approaches provides an understanding of how environmental factors affect individual dispersal, population size, and structure. We first generated a set of predictions of the expected effect of “riverscape” characteristics on salmonid genetic diversity and divergence, based on the results of earlier research on this topic in salmonid fishes. We then tested these predictions in a data set consisting of the microsatellite data and riverscape characteristics of 39 Atlantic salmon ( Salmo salar ) populations from northwest Russia. The carrying capacity of the river was an important factor shaping the genetic diversity and differentiation of Atlantic salmon populations in the region: salmon in rivers with a larger carrying capacity tended to have higher genetic diversity and lower genetic differentiation. The importance of other riverscape characteristics often varied between anadromous and freshwater populations. Taken together, these associations demonstrate a high and complex level of river landscape influence on the genetic diversity and structure of Atlantic salmon populations and highlight the importance of spawning and nursery area maintenance for the conservation of salmonids.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".