Combining genetic and isotope frameworks improves reconstruction of fish provenance across riverscapes
Bibliographic record
Abstract
Abstract Understanding the spatial ecology of migratory species is uniquely challenging using conventional approaches. In fisheries such as for Pacific salmon, genetic stock identification (GSI) and isotope‐based methods have emerged as strategies for reconstructing spatial ecology but are limited by the spatial resolution of genetic differentiation and isotopic heterogeneity. We show that integrating these complementary datasets improve the spatial resolution of provenance assignments. To do so, we reconstructed basin‐wide estimates of natal origin locations for Chinook salmon in the Yukon River using samples ( n = 247) from an experimental fishery designed to assess in‐season run timing. A combined framework improved precision of likely provenance assignments (stream km > 0.7 posterior probability) by 92% over genetic assignments and 52% over strontium isotope methods. In doing so, we illustrate watershed scale estimates of natal origin distributions with a greater resolution available from GSI or isotope data alone.
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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".