Understanding How Angler Characteristics and Context Influence Angler Preferences for Fishing Sites
Bibliographic record
Abstract
Abstract Understanding angler heterogeneity is critical for fisheries managers to be able to develop management approaches that minimize stress to fish and aquatic ecosystems while maximizing benefits to anglers. We used a stated-preference choice model of British Columbia, Canada, anglers to predict their behavioral intentions to fish at lakes described by catch-related and non-catch-related attributes, such as expected catch and travel distance. We investigated how different means of accounting for preference heterogeneity and decision context affected conclusions about angler preferences for fishing sites. Our preferred model of fishing site choice accounted for both observable and unobservable (to the researcher) preference heterogeneity for site attributes, angler characteristics (i.e., recreation specialization and residence), and context (i.e., trip duration and target species). On average, anglers’ preferences conformed to expectations, but preferences for different site attributes varied greatly among anglers and contexts. For example, highly specialized anglers were more influenced by catch rates, fish size, and bag limits and were less deterred by travel than were less-specialized anglers. Anglers facing multiple-day trip contexts were influenced more by fish size and bag limits than were anglers considering day fishing trips. This latter result is especially important, as researchers often estimate models of angler behaviors by excluding anglers that undertake multiple-day trips, who might be the most sensitive to changes in regulations and fishing quality characteristics. Received June 19, 2017; accepted September 18, 2017Published online November 10, 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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".