Predicting Fishing Participation and Site Choice While Accounting for Spatial Substitution, Trip Timing, and Trip Context
Bibliographic record
Abstract
Abstract We developed choice models to understand and predict the amount, timing, and locations of recreational fishing trips taken by anglers in northwestern Ontario, Canada. These models incorporated several improvements over previous models to account for complex patterns of spatial substitution among fishing sites, the context of fishing trips, and the importance of tradition and weather on the timing of trips. Joint models of fishing participation and site choice were developed for two resident populations of anglers from northern Ontario. For both populations, the three innovations provided significant improvements to the models and important information for understanding and predicting recreational fishing behaviors. The utility of the model to fisheries managers was illustrated through a management scenario that involved the restoration of walleyes Sander vitreus in a large water body. The forecasts suggested that the effect of this restoration on fishing effort at other waters was influenced by spatial proximity and temporal use at the fishing sites.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".