Genetic stock composition analysis of the Chinook salmon bycatch samples from the 2017 Bering Sea trawl fisheries
Bibliographic record
Abstract
A genetic analysis of samples from the chum salmon (Oncorhynchus keta) bycatch of the 2008 Bering Sea walleye pollock (Theragra chalcogramma) trawl fishery was undertaken to determine the overall stock composition of the sample set. Samples were genotyped for eleven microsatellite markers and results were estimated using the current chum salmon microsatellite baseline. In 2008, genetic samples were collected opportunistically as part of a special project, but sample biases have the potential to affect stock composition analysis results. Consequently, stock composition estimates apply to the sample set and may not represent the entire chum salmon bycatch. Based on the analysis of 629 chum salmon bycatch samples collected throughout the 2008 Bering Sea trawl fishery, North Asian (27%), East Asian (35%), and Eastern Gulf of Alaska/Pacific Northwest (24%) stocks dominated the sample set, with smaller contributions from western Alaska (7%) and Upper/Middle Yukon River (6%) stocks. The estimates for the 2008 chum salmon bycatch sample set were similar to the 2005, 2006, 2007, and 2009 chum salmon bycatch estimates, suggesting consistency of the regional stock contributions across years. Analysis of temporal groupings within the groundfish “B” season revealed changes in stock composition during the course of the season with decreasing contribution of Eastern Gulf of Alaska/Pacific Northwest stocks over time, but leaves unanswered whether these changes are due to temporal or spatial differences in the sample set.
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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 | 0.002 | 0.001 |
| 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".