Vessel dynamics of the Gulf of St. Lawrence snow crab (Chionoecetes opilio) fishery: examining spatial associations and fishing success
Bibliographic record
Abstract
Fleet dynamics are an important, but understudied aspect of fisheries management. The fleet dynamics of the Gulf of St. Lawrence snow crab (Chionoecetes opilio) fishery were examined using two approaches: 1) examining spatial associations based on homeport location, vessel knowledge of the fishery and their impacts on catch rates, and 2) applying an agent-based model to the fishery to examine impact of having pre-season survey information. There were overall higher levels of clustering of vessels who had extensive knowledge of the fishery (traditionals) and the non-traditionals were fishing in similar areas as traditional vessels. Lower levels of clustering were observed among the non-traditional vessels. There were higher total landings with pre-season survey information. Examining how vessels associate gives insight into fleet dynamics, which may be important in management decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".