Managing Canadian freshwater fisheries: persistent challenges and emerging opportunities
Bibliographic record
Abstract
Freshwater fisheries and biodiversity have substantial economic, socio-cultural, and ecological value, but face severe and mounting anthropogenic threats. Canada's freshwater fisheries are not exempt from this, and provide excellent opportunities to better understand these overlooked and undervalued systems. Using expert and chain-referral sampling, we surveyed practitioners from across Canada about the management of freshwater fisheries. We used a mixed methods approach to identify and describe in detail many important aspects of the above processes, including 10 persistent and innate challenges in (1) bureaucratic sprawl, (2) lack of priority, (3) scope, (4) competing interests, (5) political inconstancy, (6) socio-ecological complexity, (7) limited tools, (8) geographies and scale, (9) reactivity, and (10) intersectoral frictions. Many of these challenges defy conventional problem solving (e.g., advocacy, basic science), leading to chronic incapacity and triage management in some freshwater fisheries. We highlight opportunities to increase management capacity, using innovation where conventional solutions fall short (e.g., using novel technologies to increase management scope). Achieving sustainability in Canadian freshwater fisheries will require ingenuity and supportive contributions beyond those that currently exist.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".