Angling party persistence and visitation in a recreational Lake Trout fishery: Relative influence of travel distance and fuel costs
Bibliographic record
Abstract
Abstract Objective Cormack–Joly–Seber (CJS) mark–recapture analysis was used to estimate the effect of travel distance and fuel costs on angling party persistence (i.e., survival = probability of remaining in the Lake Trout Salvelinus namaycush fishery from year-to-year) and visitation (i.e., detection = probability of detection at the access point) over a 14-year period (2006–2019) for the Lake Trout recreational fishery in Lake Opeongo, Ontario, Canada. Methods Boat identification numbers, positioned near the bow and present by law, were used as “tags” in the CJS analysis. Complete trip creel interviews at the sole access point provided information on trip characteristics. Result The long-term mark–recapture analysis of individual angling parties showed travel distance had a significant negative effect on party persistence among years and visitation within years. The distance effect was strongest for visitation, with visitation having a more negative relationship with distance than persistence. Persistence in the Lake Trout fishery was insensitive to fuel costs while the fuel cost covariate had a small though significant and negative effect on visitation. Time-varying CJS models were ranked low with little model weight indicating the distance effect estimated for Lake Opeongo was a general phenomenon and not year dependent. Conclusion Cormack–Joly–Seber mark–recapture analysis cleanly separated effects of travel distance and fuel cost between visitation versus persistence for Lake Trout angling parties. Prior to this analysis, travel costs were typically associated with visiting a recreational fishery, while here we have shown that travel costs can be associated with persisting as anglers in a fishery independent of visitation patterns. Long-term monitoring of angling parties provides unique insights into angling patterns that can aid in travel cost estimation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".