Genetic stock composition analysis of Chum salmon bycatch from the 2013 Bering Sea Walleye pollock trawl fishery
Bibliographic record
Abstract
A genetic analysis of chum salmon (Oncorhynchus keta) bycatch from the 2013 Bering Sea walleye pollock (Gadus chalcogrammus) trawl fishery was undertaken to determine the overall stock composition of the sample set. Samples were genotyped for 11 microsatellite markers and results were estimated using the current chum salmon microsatellite baseline. Since 2011, genetic samples from the chum salmon bycatch were collected systematically to reduce sample biases that may exist in collections from previous years. In 2013, one genetic sample was collected for every 30.3 chum salmon caught in the 99.8% of the mid water trawl fishery that was sampled. Evaluation of sampling based on time, location, and vessel indicated that the genetic samples were representative of the total bycatch. Based on the analysis of 3,880 chum salmon bycatch samples collected throughout the 2013 Bering Sea trawl fishery, the North east Asia stocks dominated the sample set ( 45 %); moderate contributions came from Southeast Asia (15 %), Eastern Gulf of Alaska (GOA)/Pacific Northwest (PNW) (15%), and Western Alaska (18%) stocks, and smaller contributions came from Upper/Middle Yukon River (6 %) and Southwest Alaska (1%) stocks. The regional stock estimates for the 2013 chum salmon bycatch were similar to those for the 2012 bycatch, but differed significantly from estimates for other years, especially for the Asian and the Eastern GOA/PNW regions. There were significant spatial differences in stock distribution with the South east Asia contribution higher in the northwestern U.S. waters of the Bering Sea than in the southeastern Bering Sea, and the Eastern GOA/PNW contribution highest in the easternmost area sampled in the southeastern Bering Sea. Analysis of temporal strata revealed changes in stock composition during the course of the fall "B" season with increasing contribution of Northeast Asia stocks, decreasing contribution of Eastern GOA/PNW stocks, and variable contribution from Southeast Asia and Western Alaska.
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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 | 0.000 |
| 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 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".