Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—With half a million lakes, 4,300 rivers, and one of the largest rivers in the world that connects with the Atlantic Ocean, the province of Québec has a unique geographic context that favors a diversity of aquatic habitats and thriving fish communities. By legislative delegation, the provincial government is responsible for the management of freshwater, anadromous, and catadromous species. Each year, 43 million fish are harvested by recreational anglers and about 400 tons by commercial fishers, so fisheries management and conservation must rely on robust monitoring networks, strong scientific studies, and constant collaborations with a diverse array of partners. The aim of this chapter is to present a portrait of Québec’s fish community, the legislative framework, and the scientific background behind fisheries management and conservation in Québec. We present eight case studies highlighting the variety of challenges faced in fisheries management.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".