The Longspine Thornyhead Fishery along the West Coast of Vancouver Island, British Columbia, Canada: Portrait of a Developing Fishery
Bibliographic record
Abstract
A fishery for thornyhead Sebastolobus spp. started in the early 1990s along the Pacific coast of Canada, primarily in response to market demand from Japan. As the fishery evolved, the deepwater longspine thornyhead S. altivelis became a desirable species because it is easily targeted and commands high prices. Its congener, shortspine thornyhead S. alascanus, largely remained a bycatch species in other mid-depth fisheries along the continental slope. This paper describes the developing longspine thornyhead fishery off the western coast of Vancouver Island, British Columbia. We illustrate how the fishery migrated from its place of origin, possibly to maintain high catch rates as the resource was depleted. Analyses of the factors that significantly influence catch rates demonstrate that a model without factor interactions does not adequately describe the data. Length analysis shows that longspine thornyheads decrease in size with depth, a trend opposite to that for shortspine thornyheads. We also investigate the diversity of fish species caught in longspine thornyhead tows using the Shannon–Wiener index and find that diversity decreases with depth and increases in a northerly direction. Finally, we highlight issues of broad interest to managers of developing fisheries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".