A Broad-Scale Approach to Management of Ontario's Recreational Fisheries
Bibliographic record
Abstract
Abstract Sustainable exploitation of Ontario's aquatic resources calls for a new management approach. This vast resource includes more than 250,000 lakes and offers angling opportunities for many popular species (e.g., walleye Sander vitreus (formerly Stizostedion vitreum), lake trout Salvelinus namaycush, brook trout S. fontinalis, northern pike Esox lucius, smallmouth bass Micropterus dolomieu, largemouth bass M. salmoides, and muskellunge E. masquinongy). In pioneer days, the “apparently inexhaustible abundance of resources” fostered an open-access policy promoting the recreational use of these resources for the benefit of the economy. After World War II, there was a rapid increase in angling effort and by the 1970s many lakes were being overexploited. Clearly, an unrestricted, open-access policy was no longer appropriate. The result has been a rapid proliferation of fishing regulations as exceptions to divisionwide regulations that were created to protect lakes where problems were detected. The growing complexity of these regulations is the result of a management approach that has focused on individual lakes. This complexity is not popular with the angling public, and evaluation of its benefits has proven difficult because a change in regulations on one lake may affect fishing effort on other lakes. We argue that a larger spatial and temporal scale of management is needed when a resource is widely dispersed across a large population of lakes. This new approach should incorporate (1) consensus on biologically achievable objectives, (2) periodic, unbiased assessment of the state of the resource, (3) periodic evaluation to decide whether current management practices are meeting objectives, and (4) adaptive management in choosing among alternative management actions. Recent progress towards establishing this management approach in Ontario is discussed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".