Adaptation genomics: next generation sequencing reveals a shared haplotype for rapid early development in geographically and genetically distant populations of rainbow trout
Bibliographic record
Abstract
Local adaptation occurs when a population evolves a phenotype that confers a selective advantage in its local environment, but which may not be advantageous in other habitats. Restricted gene flow and strong selection pressures are prerequisites for local adaptation. Fishes in the family Salmonidae are predicted to provide numerous examples of local adaptation because of the high fidelity of returning to spawn in their natal streams, which results in highly structured populations, and the wide diversity of environments that salmonids have colonized. These conditions are ideally suited for producing a set of specialist phenotypes, whose fitness is maximized for one specific habitat, rather than a generalist phenotype similarly viable in several environments. Understanding patterns and processes leading to local adaptations has long been a goal of evolutionary biology, but it is only recently that identifying the molecular basis for local adaptation has become feasible because of advances in genomic technologies. The study of shared adaptive phenotypes in populations that are both geographically distant and genetically distinct should reveal some of the fundamental molecular mechanisms associated with local adaptation. In this issue of Molecular Ecology, Miller et al. (2012) make a significant contribution to the development of adaptation genomics. This study suggests that salmonids use standing genetic variation to select beneficial alleles for local adaptations rather than de novo mutations in the same gene or alternative physiological pathways. Identifying the genetic basis for local adaptation has major implications for the management, conservation and potential restoration of salmonid populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".