Estimation of seabird bycatch for North Pacific longline vessels using design- and model-based methods
Bibliographic record
Abstract
Bycatch estimation for sensitive species is becoming increasingly important with the shift toward an ecosystem-based approach to fisheries management. Incidental mortalities for various seabird species occur on longline vessels throughout the world, including those in the North Pacific groundfish fleet. We present an approach to seabird bycatch estimation for North Pacific longline vessels using observer-collected data. Observers collect enormous amounts of data through a complex sampling design, but some information deficiencies preclude bycatch estimation using only probability sampling. Our approach combines probability sampling with model-dependent techniques to overcome these information deficiencies. The resulting bycatch estimator reflects the observer sampling design as closely as possible and minimizes reliance on untested model assumptions. We apply our estimator to black-footed albatross (Phoebastria nigripes) bycatch as an example and compare yearly estimates to those previously published. We also suggest changes in data collection that would further reduce dependence on model assumptions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".