Effect of Trap Soak-Time on the Trap-Selectivity Profile and By-Kill in Prawn-Trap Fisheries
Bibliographic record
Abstract
Abstract: Canada’s Department of Fisheries and Oceans seeks to manage British Columbia’s prawn fishery by limiting the season length, vessel entry, and the number of prawn traps per vessel. However, fishers can still adjust their effort by increasing the number of trap lifts during the season. This study examines the effect of trap soak-time on size-selectivity, looks at how it translates to by-kill, and reviews the traditional management responses. The management recommendations in this paper focus on optimizing the interaction between CPUE, by-kill, enforcement costs, and fisher responses. Peer’s law (the solution to a problem changes the problem) predicts that trying to solve a fishery common-property resource problem only changes the problem’s expression. Thus, limiting entry changes a too-many-fishers issue to a capital-stuffing problem, limiting gear changes a capital-stuffing problem to a gear-use issue, and regulating gear use leads to other problems and ever more micro-management. Thus, regulation and related costs have become part of the problem of rent dissipation and poverty in fishery dependent communities. Efforts to fine-tune regulations in BC’s prawn fishery have led to an ever expanding spiral of costly, clumsy, and intrusive regulations, enforcement, and related procedures that dissipate resource rents, frustrate fishers, and ultimately are ineffective in protecting the resource and/or the associated jobs. The best way out of this morass appears to be for managers of sedentary species to confine their efforts to macro-management regulations that focus on limiting the consequences of fishing and ensuring that individual fishers endure the consequences of their actions.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".