Data from: The demographic history of Atlantic salmon (Salmo salar) across its distribution range reconstructed from approximate Bayesian computations
Bibliographic record
Abstract
Understanding the dual roles of demographic and selective processes in the buildup of population divergence is one of the most challenging tasks in evolutionary biology. Here, we investigated the demographic history of Atlantic Salmon across the entire species range using 2035 anadromous individuals from North America and Eurasia. By combining results from admixture graphs, geo-genetic maps and an Approximate Bayesian Computation (ABC) framework, we validated previous hypotheses pertaining to secondary contact between European and Northern American populations, but also identified secondary contacts in European populations from different glacial refugia. We further identified the major sources of admixture from the southern range of North America into more northern populations along with a strong signal of secondary gene flow between genetic regional groups. We hypothesize that these patterns reflects the spatial redistribution of ancestral variation across the entire North American range. Results also support a role for linked selection and differential introgression that likely played an under-appreciated role in shaping the genomic landscape of species in the Northern hemisphere. We conclude that studies between partially isolated populations should systematically include heterogeneity in selective and introgressive effects among loci to perform more rigorous demographic inferences of the divergence process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".