Quantifying bycatch risk factors for the Chinese distant water fishery
Bibliographic record
Abstract
Abstract Mitigating bycatch of non-target fish species is a common objective in fisheries management that may be supported by the gathering of data from fishery observer programs and quantitative analysis of bycatch risk factors. We build three GLM models based on Chinese tuna longline fishery observer data in terms of analysis of total bycatch rate (TB rate), total bycatch ratio (TB ratio), and species-specific bycatch rate, respectively. The positive log-linear models assumed a Gaussian observation error model and a linear combination of categorical independent variables, including area, year, month, depth, and bycatch species. Results show that distributions of TB rate and TB ratio followed different trends and a latitudinal decrease was observed from both the northern and southern hemisphere of the equator. Comprehensively, the Pacific is a better place to fish compared to the Indian and Atlantic Oceans in terms of relatively lower TB rate and TB ratio. Fishing in open oceans can somehow avoid a high TB ratio than fishing in coastal waters. As a result, we recommend area 2SW, 2SE, 2 NW, 14SW, 14SE, and 14 NW as appropriate fishing ground for Albacore (Thunnus alalunga) while area 11N, 11S, 18SW, and 18SE to be appropriate fishing ground for fishing bigeye tuna (Thunnus obesus). Setting fishing gears deeper than 500 m would also help to get a low TB rate and TB ratio.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".