Identifying hidden biocomplexity and genomic diversity in Chinook salmon, an imperiled species with a history of anthropogenic influence
Bibliographic record
Abstract
Biocomplexity is an important mechanism for population resilience in changing environments. However, we are just beginning to understand how to identify biocomplexity so that species management efforts promote resilience and stability. Genomic techniques are emerging as an important method for identifying biocomplexity. Central Valley (CV) Chinook salmon are an example of a species at risk of extinction if better methods for identifying and protecting biocomplexity are not employed. To address this knowledge gap, we employed restriction site associated DNA sequencing to conduct the first genomic study of all major populations of CV Chinook salmon. We found greater population structure across the Central Valley than previously documented. Additionally, we show evidence for differentiation and adaptation within migratory phenotypes despite high levels of gene flow. We also determined that genomic data can vastly improve our ability to assign individuals to their natal populations, even as they mix during migration, a finding that will assist management practices. These results demonstrate how genomic study can greatly improve our ability to identify and conserve biocomplexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".