Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Canada has rich freshwater resources, with millions of lakes and wetlands, and hundreds of thousands of kilometers in rivers and streams. These waters are home to diverse fisheries that support subsistence, commercial, and recreational fisheries and generate numerous ecosystem services (e.g., culture, nutrition, leisure, livelihoods, nutrient cycling). Indigenous fisheries in Canada have existed for millennia, whereas recreational and mainstream commercial fisheries are more recent developments, attributable to European settlers. Canada has an extensive, but imperfect, history of fisheries management. As such, there can be no one Canadian context, especially considering the broad geography and diverse fisheries of Canada. Today, there is growing recognition of the role of co-management and other shared governance structures with Indigenous governments and communities. Canada is also well known for its early innovations and expertise in freshwater fisheries science. Moving forward, there are opportunities to research and govern in ways that ensures the sustainable and equitable management of our freshwater fisheries by integrating new tools (e.g., remote sensing, many “-omics”) and ways of thinking (e.g., social-ecological systems approaches, co-production with stakeholders and rights-holders, enhanced governance of shared resources).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".