Improving compliance of recreational fishers with Rockfish Conservation Areas: community–academic partnership to achieve and evaluate conservation.
Bibliographic record
Abstract
Compliance is a key factor in ensuring success of marine conservation. We describe a community-academic partnership that seeks to reduce non-compliance of recreational fishers with Rockfish Conservation Areas (RCAs) around Galiano Island in British Columbia, Canada. Previous work showed mostly unintentional non-compliance by recreational fishers. From 2015-2018 we developed and implemented outreach and public education activities. We distributed information at community events, and installed 46 metal signs with maps of nearby RCAs at marinas, ferry terminals, and boat launches. During the summers of 2015, 2017, and 2018, we interviewed 86 recreational fishers to gauge their compliance with RCAs. Compared to a baseline in 2014, there was a reduction of 22% (from 25% to 3%) of people who unintentionally fished in RCAs with prohibited gears. In 2018, 67% of participants had seen our outreach materials. We used trail cameras overlooking RCAs to assess non-compliance in six locations on Galiano Island. Illegal fishing incidents within RCAs declined from 42% of days monitored in 2014 to 14% in 2018. While our outreach efforts were limited in scale and scope, they appear to be making a difference. Our activities and findings can provide guidance for other regions seeking to improve compliance by recreational fishers.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".