Do Fish Drive Recreational Fishing License Sales?
Bibliographic record
Abstract
Management agencies need to understand the factors that influence fishing license purchases. While traits such as gender can influence the decisions of recreational fishers, a gap remains in understanding the influence of catch-related fishing quality on these decisions. We evaluated the use of fish biomass density as a proxy for catch-related fishing quality along with non-catch-related factors (population density, gender, and ethnicity) to explain variation in 2014 resident fishing license rates across 510 origins in Ontario. License rates were higher in areas with lower population density (i.e., rural areas), in areas with higher fish biomass density, and among populations with stronger representation by ethnic majorities. From simulated scenarios, we predicted that resident license sales could increase between 14% and 25% if fish biomass density increased by 30% and 54% in northeastern and southern Ontario, respectively. However, license sales could decrease between 5% and 10% with a 30% redistribution of rural residents to a major urban area. The relationship between license rates and non-catch-related factors confirms the role of urbanization on recreational fishing participation, while catch-related factors provide support for a functional response by fishers to fish. Received April 28, 2016; accepted October 3, 2016Published online January 3, 2017
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".