Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—The ecological and economic significance of freshwater commercial fishing in Canada is substantial, with roughly 0.6 kg of freshwater fishes harvested per Canadian resident in 2019. We review the historical and present-day commercial catch of live baitfishes in Ontario, which represents an overlooked yet significant component of commercial fishing in Canada. Live baitfishes represented the sixth most harvested freshwater species group by landed weight in Ontario in 2018, surpassing food fishes such as Lake Trout Salvelinus namaycush and sunfishes Centrarchidae spp. While a rotational harvest strategy initially developed in the 1930s–1960s to increase yield and improve sustainability, it has led to unique trade-offs involving bycatch and the inadvertent redistribution of live, noncommercial species, including invasive fishes. Implications of invasive fish bycatch are challenging given overarching fisheries management paradigms focused on native species restoration and reducing the spread of invasive species. We illustrate the application of quantitative tools to manage bycatch of noncommercial species, describe the potential ecological concerns of invasive bycatch contained inadvertently within the fishery, and present opportunities for future management of this ecologically and economically significant freshwater fishery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".