Genetic differentiation of Alaska Chinook salmon: the missing link for migratory studies
Bibliographic record
Abstract
Most information about Chinook salmon genetic diversity and life history originates from studies from the West Coast USA, western Canada and southeast Alaska; less is known about Chinook salmon from western and southcentral Alaska drainages. Populations in this large area are genetically distinct from populations to the south and represent an evolutionary legacy of unique genetic, phenotypic and life history diversity. More genetic information is necessary to advance mixed stock analysis applications for studies involving these populations. We assembled a comprehensive, open-access baseline of 45 single nucleotide polymorphisms (SNPs) from 172 populations ranging from Russia to California. We compare SNP data from representative populations throughout the range with particular emphasis on western and southcentral Alaska. We grouped populations into major lineages based upon genetic and geographic characteristics, evaluated the resolution for identifying the composition of admixtures and performed mixed stock analysis on Chinook salmon caught incidentally in the walleye pollock fishery in the Bering Sea. SNP data reveal complex genetic structure within Alaska and can be used in applications to address not only regional issues, but also migration pathways, bycatch studies on the high seas, and potential changes in the range of the species in response to climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".