Freshwater Fisheries in Canada: Historical and Contemporary Perspectives on the Resources and Their Management
Bibliographic record
Abstract
Abstract.—Ontario’s inland waters provide a wide range of aquatic ecosystems that support the highest freshwater fish diversity in Canada. The distribution of fish across the province is a result of postglacial recolonization, climate, and human influence. These resources support Indigenous fishing, some commercial fishing, and a very large recreational fishery whose value exceeds Can$1.3 billion annually in direct expenditures and investments (excluding the Great Lakes proper). Management of Ontario’s inland fisheries has changed rapidly in recent years as the province has confronted the challenge of managing such a vast landscape. Recognition of the challenge has fostered a landscape level approach in setting goals, assessing stress, taking management action, and monitoring its outcomes. In this chapter, we show how this approach is being used to track changes in the state of Ontario’s inland fish and fisheries and to support management decisions. We describe the geographic setting, showing how heterogeneity across this landscape has created diversity among lakes and rivers, affecting species composition and potential fish production. We describe the current management framework and demonstrate how existing monitoring programs offer a broadscale view of resource status in different areas of the province. Lastly, we document current issues and comment on management responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".