Temporal and spatial patterns of angler effort across lake districts and policy options to sustain recreational fisheries
Bibliographic record
Abstract
Studies suggest anglers allocate fishing effort across lakes districts based on fishing quality and travel time resulting in high effort near urban areas, which declines with distance. This results in quality fisheries in remote areas and poorer quality near population centres. In this paper we explore the effectiveness of harvest and effort regulations to counter this tendency for overfishing and stock collapse for a rainbow trout ( Oncorhynchus mykiss ) fishery from a lake district in British Columbia, Canada. Our results suggest that daily bag limits can improve fishing quality if the effort is not too high, but fail to prevent collapse close to population centres. The ability of complete catch-and-release regulations to maintain quality fisheries is inversely related to the rate of release mortality. Catch-and-release fisheries with low mortality can maintain quality close to large cities, whereas higher release mortality does not prevent collapse. Direct fishing effort limitation can maintain quality fisheries, but a high proportional reduction in effort is required to maintain quality near population centres. Explicit consideration of the location of fisheries within lake districts is necessary to design effective management approaches and will likely require a mixed strategy with substantial spatial variation in harvest control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".