Data from: Genetic evidence for ecological divergence in kokanee salmon
Bibliographic record
Abstract
The evolution of locally adapted phenotypes among populations that experience divergent selective pressures is a central mechanism for generating and maintaining biodiversity. Recently, the advent of high-throughput DNA sequencing technology has provided tools for investigating the genetic basis of this process in natural populations of non-model organisms. Kokanee, the freshwater form of sockeye salmon (Oncorhynchus nerka), occurs as two reproductive ecotypes, which differ in spawning habitat (tributaries vs. shorelines), however outside of the spawning season the two ecotypes co-occur in many lakes and lack diagnostic morphological characteristics. We used restriction site associated DNA (RAD) sequencing to identify 6,145 SNPs and genotype kokanee from multiple spawning sites in Okanagan Lake (British Columbia, Canada). Outlier tests revealed 18 loci putatively under divergent selection between ecotypes, all of which exhibited temporally stable allele frequencies within ecotypes. Six outliers were annotated to sequences in the NCBI database, two of which matched genes associated with early development. There was no evidence for neutral genetic differentiation, however, outlier loci demonstrated significant structure with respect to ecotype and had high assignment accuracy in mixed composition simulations. The absence of neutral structure combined with a small number of highly divergent outlier loci is consistent with theoretical predictions for the early stages of ecological divergence. These outlier loci were then applied to a realistic fisheries scenario in which additional RAD sequencing was used to genotype kokanee collected by trawl in Okanagan Lake, providing preliminary evidence that this approach may be an effective tool for conservation and management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".