Standardized Broad-Scale Management and Monitoring of Inland Lake Recreational Fisheries: An Overview of the Ontario Experience
Bibliographic record
Abstract
Abstract There are ~250,000 lakes in Ontario that support important cultural, recreational, and economic fisheries. In 2005, the Ontario Ministry of Natural Resources and Forestry adopted the Ecological Framework for Recreational Fisheries Management to tackle the heterogeneity of lake resources and angler mobility across the landscape, increase public participation in fisheries management, and streamline an ever-growing list of regulations. The Broad-Scale Monitoring Program for Inland Lakes began in 2008 to meet these goals. Essential elements of the program are: clear objectives, standardized sampling methods, operational implementation, diagnostic indicators, standardized reporting, a multidisciplinary team, and adaptive monitoring. Fishes, zooplankton, habitat, and angling activity are measured at each lake and provide the data needed to make evidence-based fisheries management decisions. The data have benefited other provincial initiatives and provided significant contributions to the science of freshwater ecology. Recommendations are provided for other jurisdictions considering the implementation of a standardized broad-scale monitoring program.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".