Putting Fishers ’ Knowledge to Work – Conference Proceedings, Page 366 HISTORICAL AND CURRENT KNOWLEDGE OF THE GREENLAND HALIBUT FROM QUÉBEC FIXED-GEAR FISHERS IN THE GULF OF ST.
Bibliographic record
Abstract
Fishing for Greenland halibut (Reinhardtius hippoglossoides) in the estuary and the Gulf of St. Lawrence (NAFO divisions 4RST) has been practiced mainly with gillnets by coastal fishers of Quebec and Newfoundland since the beginning of the 1970s. However, little information is available on the development of this exploitation, for example, on the evolution of the fishing practices. In 1997, a project on the Greenland halibut fishers ’ knowledge was initiated with the aim of documenting the historical and current knowledge of this fishery. The specific objectives were to compile a qualitative database of information from the fishers and to integrate this information into stock status assessments. Semi-directed individual interviews were held with 21 fishers. The information collected touched on four themes: the fishing practices, the biology and environment of the Greenland halibut, the social dimension of the fishing activities, and the management and conservation of this species. The results presented here describe the changes in the fishing practices and strategies that took place between 1970 and 1997. We also examined the relationships between these changes, the prevailing socio-economic context, and the landings of Greenland halibut for the same period. In thirteen years, the Greenland halibut fishing has evolved from a traditional and subsistence fishery to an effective and competitive commercial exploitation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".