Monitoring bycatch: a fishing industry generated solution
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.The groundfish industry in the province of British Columbia and the Department of Fisheries and Oceans Canada implemented the B.C. Groundfish Integrated Pilot Project in March of 2006. The project was initiated, in general, because of the difficulty of managing many species of groundfish across multiple license/gear sectors and, in particular, because of the difficulty in quota-managing stocks without discard information. Motivated, in part, by a combination of “carrot” (introduction of ITQs) and “big stick” (fix it or lose it) incentives, the fishing industry took the lead role in designing, funding, and implementing a cost-effective 100% at-sea catch monitoring program in a small-boat fleet of over 250 vessels. In its first three years, the Project has surpassed the expectations of many of the industry and government participants. This monitoring now provides accurate and statistically defensible estimates of total catch for all quota species, thereby removing the need for more complex, and possibly biased, discard estimation procedures. The accurate monitoring of total catch by species (discarded and retained) by each vessel in near real time provides managers with relatively simple options for controlling and even minimizing discards through individual species caps. With this individual accountability framework, fishers are motivated to develop their individualized strategies to reduce non-desirable catches, as opposed to the more problematic approach of top-down design and enforcement of temporal or spatial closures, or gear restrictions. The presentation focuses on the importance of a bottom-up industry-driven solution to the problem, the key elements of the monitoring, and the results of a test case to assess the accuracy of the monitoring.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".