Predicting differences in angler beliefs, threat perceptions, and actions in British Columbia's rainbow trout and steelhead fisheries
Bibliographic record
Abstract
Anglers are a diverse population whose behaviours and perspectives are influenced by a myriad of factors including knowledge, expertise, management actions, and regulations. We examine similarities and differences in behaviours and perspectives among freshwater anglers of rainbow trout and steelhead (Oncorhynchus mykiss) in British Columbia, Canada, using an online survey. Findings from the survey suggest that subgroups or “types” of anglers are identifiable by differences in their behaviours and perspectives according to geographic area, gear type, fishery, and frequency of fishing activities. Our results indicate that angler types share many of the same motivations for engaging in fishing behaviours and similar concerns regarding threats to their preferred fishery; however, differences were evident across types of issues related to angler behaviour, as well as views on fisheries management. Overall, we argue that understanding fishery-scale angler heterogeneity can benefit fisheries management by highlighting areas of agreement and disagreement and encouraging tailored communications and relationship-building with important angler subgroups.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".