How do ice anglers and their fishing behaviors differ from non-ice anglers? Insights drawn from a large-scale survey of anglers from Ontario, Canada
Bibliographic record
Abstract
Abstract Objective The research goal was to understand who are ice and non-ice (i.e., open water only) fishers and how do they fish (e.g., target species choice and travel distance to fishing sites). To achieve this goal, hypotheses were developed and tested that ice fishing participation and fishing behaviors would be influenced by (1) the availability of quality ice fishing opportunities, (2) the commitment level of the angler to fishing, and (3) the urban or rural location of residence and gender identity of an angler. Methods Differences between ice and non-ice fishers from Ontario, Canada, were assessed using inferential statistics and general linear models and cross tabulations from responses to a large-scale survey of resident Ontario anglers in 2020. Result Ice fishing participation rates were higher among fishers who resided in areas with longer (better quality) ice fishing seasons; showed increased commitment to fishing by purchasing more expensive fishing licenses and fishing more days during the open-water season; and were rural residents, males, and younger individuals. When compared to open water, ice fishing activity was more spatially constrained and heavily targeted towards fish species that prefer cool- or coldwater habitats. Even among ice fishers, these individuals targeted cool- and coldwater fish species at higher rates during the ice than open-water fishing seasons. Conclusion Ice fishers differ from non-ice fishers in who they are (more likely to be male, rural, and younger), how they connect to fishing (more likely to be committed to fishing), and how they fish (more likely to target cool- and coldwater species). These differences suggest that some water bodies (e.g., those near angling populations that hold coldwater fish species) may be at a greater risk from being overexploited during the ice than open-water season.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".