Muskellunge Management: Fifty Years of Cooperation Among Anglers, Scientists, and Fisheries Biologists
Bibliographic record
Abstract
Abstract.—Muskies Canada Inc. (MCI) has represented Muskellunge Esox masquinongy anglers since 1978, advocating for the conservation and effective management of Muskellunge populations. A core initiative of MCI, since its inception, has been the voluntary Angler Log Program (ALP), which collects data on MCI-member Muskellunge angling effort and catch. These data are shared with the Ontario Ministry of Natural Resources and Forestry with the intent of contributing to the management of Muskellunge fisheries in Ontario. This paper examines the data provided by MCI members of the six water bodies with the highest representation in the ALP from 1995 to 2015—Pigeon Lake, Rideau River, Lake St. Clair, Georgian Bay, St. Lawrence River, and Ottawa River. Mean length, catch per unit effort, and proportional size are examined to determine (1) if a response to large-scale changes in fish abundance (viral hemorrhagic septicemia-related die-offs) can be detected in the data, and (2) if data from the ALP relate to the broad management objectives for the fishery. While the ALP is subject to some sources of bias, our assessment suggests that there is considerable potential for direct use of the data in setting and measuring fishery, and Muskellunge population, objectives.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".