Differentiating salmonid migratory ecotypes through stable isotope analysis of collagen: Archaeological and ecological applications
Bibliographic record
Abstract
The ability to distinguish between different migratory behaviours (e.g., anadromy and potamodromy) in fish can provide important insights into the ecology, evolution, and conservation of many aquatic species. We present a simple stable carbon isotope (δ13C) approach for distinguishing between sockeye (anadromous ocean migrants) and kokanee (potamodromous freshwater residents), two migratory ecotypes of Oncorhynchus nerka (Salmonidae) that is applicable throughout most of their range across coastal regions of the North Pacific Ocean. Analyses of kokanee (n = 239) and sockeye (n = 417) from 87 sites spanning the North Pacific (Russia to California) show that anadromous and potamodromous ecotypes are broadly distinguishable on the basis of the δ13C values of their scale and bone collagen. We present three case studies demonstrating how this approach can address questions in archaeology, archival, and conservation research. Relative to conventional methods for determining migratory status, which typically apply chemical analyses to otoliths or involve genetic analyses of tissues, the δ13C approach outlined here has the benefit of being non-lethal (when applied to scales), cost-effective, widely available commercially, and should be much more broadly accessible for addressing archaeological questions since the recovery of otoliths at archaeological sites is rare.
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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 | 0.001 | 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".