Genome-wide analysis reveals regional patterns of drift, structure, and gene flow in longfin smelt (<i>Spirinchus thaleichthys</i>) in the northeastern Pacific
Bibliographic record
Abstract
The southernmost stock of longfin smelt (Spirinchus thaleichthys) is approaching extirpation in the San Francisco Estuary (SFE); however, patterns of genetic structure, diversity and gene flow which are vital for management are poorly understood in this species. Here, we use genome-wide data to evaluate population structure of longfin smelt across a broad latitudinal scale across estuaries ranging from the SFE to Yakutat Bay and Lake Washington, and fine scale within the Fraser River and the SFE. Results indicate high genetic structure between major estuaries, fine-scale structure within the Fraser River, and low levels of structure within the SFE. Genetic structure was more pronounced between northern estuaries whereas southern estuaries showed shared ancestry and ongoing gene flow, most notably unidirectional northward migration out of the SFE. Furthermore, we detected signatures of local adaptation within the Fraser River and the Skeena River estuaries. Taken together, our results identify broad patterns of genetic diversity in longfin smelt shaped by co-ancestry, unidirectional migration and local adaptation. Results also suggest that the SFE population is genetically distinct from northernmost populations and an important source for maintaining nearby 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".