Standardized Broad-Scale Management and Monitoring of Inland Lake Recreational Fisheries: An Overview of the Ontario Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it