Modelling relationships between road access and recreational fishing site choice while accounting for spatial complexities
Bibliographic record
Abstract
This study examined the relationships between road access and the fishing site choices of northern Ontario recreational anglers. A revealed preference choice model (random utility model) was estimated with fishing trip data from an angling diary with resident anglers from the Thunder Bay and Wawa areas. The results showed that poor quality gravel roads and trails heavily and negatively impacted fishing site choices by Thunder Bay anglers who fished only during the open water season. Poorer quality roads and trails had much less impact on the fishing site choices of other Thunder Bay anglers. Wawa area anglers were, on average, less impacted by poor quality roads and trails than were Thunder Bay area anglers. Several methods of incorporating spatial complexities into the fishing site choice models were also investigated. First, an accessibility attribute was included in the models to account for potential spatial cognitive limitations of anglers when choosing fishing sites. While this attribute had a significant effect in the models, the effect was different for Thunder Bay and Wawa area anglers. A second spatial measure focused on whether anglers took fishing trips near their previously chosen fishing sites. Anglers often took fishing trips back to the fishing sites they previously chose. Thunder Bay area anglers also tended to take fishing trips that were close to their previously chosen fishing site. Finally, various generalized extreme value models were used to determine if nearby sites have correlated unobserved utilities. Results from a cross-nested logit model, which permit researchers to allocate fishing alternatives into more than one nest, showed that spatially near fishing alternatives shared some unobserved utility. Therefore, nearby fishing sites were better substitutes than were far away fishing sites. Generalized nested logit models were estimated to assess whether one global parameter could capture the correlation pattern among the unobserved utilities for the fishing sites. A global parameter was rejected in favour of nest specific parameters. While not truly a local level analysis, the generalized nested logit model was sufficient to capture some spatial heterogeneity present in the correlations among the unobserved utilities.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".