Using acoustic data from fishing vessels to estimate walleye pollock (<i>Theragra chalcogramma</i>) abundance in the eastern Bering Sea
Bibliographic record
Abstract
Eastern Bering Sea walleye pollock ( Theragra chalcogramma ) support one of the world’s largest fisheries. Because of walleye pollock’s high recruitment variability and relatively short life span, timely and accurate abundance indices are needed for fisheries management. Walleye pollock are surveyed biennially with an acoustic-trawl (AT) survey and annually with a bottom trawl (BT) survey. The latter tracks the demersal portion of the population using chartered fishing vessels, whereas the AT survey tracks the younger, midwater portion using research vessels and is critical for evaluating prerecruit abundances. Acoustic data collected from commercial fishing vessels conducting the BT survey were analyzed to provide information on midwater walleye pollock abundance at relatively low cost. A retrospective analysis of AT survey data identified a suitable index area to track midwater walleye pollock abundance. The BT survey acoustic data in that area tracked the AT survey abundance and captured its broad spatial patterns. This study is unique because commercial vessel acoustic data were used to estimate a new annual abundance index whose performance can be evaluated by a biennial research vessel survey. The new index will benefit managers by providing more accurate information on near-term abundance trends when dedicated research ship time is not available.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".