Potential of communication devices for estimating the fishing effort of purse seine fleets
Bibliographic record
Abstract
Fishing impacts the marine environment significantly, and quantifying this impact requires precise fishing effort data. This study explores the challenges associated with accurately estimating fishing effort by purse seiners and proposes a solution using records collected from real-time communication devices on fishing fleets. The estimation of fishing effort based on high-frequency GPS data can be verified with the onboard visual records. Additionally, by linking vessels within a fleet, the method utilizes information from carriers (vessels that transport fish) to enhance the estimation. Through the use of generalized additive models, this study effectively estimates the fishing effort of Japanese purse seiners, demonstrating their accuracy. Furthermore, by incorporating carrier information, models based on matched records prove to have superior predictive performance compared to those based on fishing vessel or carrier records alone. These findings lay the foundation for the potential of this approach to provide precise and cost-effective information for sustainable fishery management. The affordability of GPS devices and the common requirement of communication devices across various fleets further support the feasibility of implementing this approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".