Strontium isotope analyses (87Sr/86Sr) of otoliths from anadromous Bering cisco (Coregonus laurettae) to determine stock composition
Bibliographic record
Abstract
Abstract A commercial fishery targeting the anadromous Bering cisco (Coregonus laurettae) is occurring in the Yukon River, Alaska, USA. All three of the known global spawning populations occur in Alaska. Managers believed that two of the three populations were being harvested in the fishery. To determine the likelihood of a mixed-stock fishery, we used 87Sr/86Sr values from the freshwater region of otoliths, from spawning adult Bering cisco of known origin (n = 82), to create a baseline. A 10-fold cross-validated, quadratic discriminant function analysis (DFA) of the three baseline population 87Sr/86Sr values (Yukon River, n = 27; South Fork Kuskokwim River [Kuskokwim River], n = 25; and Susitna River, n = 30) correctly reclassified 98.8% of the fish analysed. The baseline DFA model was then used to classify the 87Sr/86Sr values from a set of otoliths removed from commercially harvested Bering cisco (n = 139). Using a posterior probability threshold of 90%, we found that >97% of the commercial samples were classified as originating in the Yukon River. The remainder of the commercial samples were classified as originating in the Kuskokwim River (0.7%) or from the Susitna River (1.5%). The presence of 87Sr/86Sr values consistent with the Susitna River discovered in the Yukon River baseline (n = 1) and commercial samples (n = 2) suggested either multiple isotope signatures within the Yukon River population or straying among populations. Strontium isotope data provide an effective tool to monitor the movements and stock composition of Bering cisco.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".