Modeling seasonal variation in crabbing effort: Which potential drivers and constraints are most influential?
Bibliographic record
Abstract
Abstract Objective Developing credible representations about how crabbing effort may vary in response to changing conditions, such as catch rates and landings prices, is often required to model the potential outcomes of different policy options. Statistical analysis of seasonal effort data for this purpose, however, has received relatively little attention. This study evaluates the ability of alternative models that include observed fishery components to explain variation in weekly effort in the seasonal Dungeness crab Cancer magister fishery in Hecate Strait, British Columbia. A principal feature of weekly effort is that it tends to decrease linearly as an annual cohort of crabs is harvested over each annual fishing season but still shows considerable variability between weeks and between seasons. Methods Seasonal models were fitted to observed catch and effort data, provided by the Area A Crabbers Association and Fisheries and Oceans Canada. Weekly effort data was modeled using the previous week’s catch, estimated stock abundance, or the catch per unit effort. Several different models were compared, and the best-fitting model was selected using Akaike information criterion. The trap hauls were recorded by the fleet’s vessel monitoring systems. Results A seasonal model associating effort (e.g., trap hauls) with the previous week's total catch best explained weekly effort for all five fishing seasons evaluated (2003–2007); the previous week's catch fitted better than models that used catch per trap haul or previously estimated in-season abundance. The best-fitting models explained the variation in weekly crabbing effort with R2 values ranging from 0.88 to 0.95. Conclusions Landed catch in the previous week explained weekly effort better than either estimated abundance or catch rate in the previous week. This result is credible because crabbers may base their decisions on whether to go crabbing using reports of crab landings in the previous week and this information should be more readily available to them than information on the abundance of harvestable crabs and fleetwide catch rate in the previous week.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".