Predicting differences in angler beliefs, threat perceptions, and actions in British Columbia's rainbow trout and steelhead fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it