Engaging the Recreational Angling Community to Implement and Manage Aquatic Protected Areas
Bibliographic record
Abstract
Recreational angling is a popular leisure activity, the quality of which is greatly dependent on fish abundance and well-functioning aquatic ecosystems. Aquatic protected areas (APAs) are used to help maintain and even restore aquatic systems and their associated biota, including fish species that are popular with recreational anglers. Paradoxically, the use of APAs has been a source of much contention and conflict between members of the recreational angling community and those interested in or mandated to protect aquatic resources on the basis of the interests of multiple stakeholder groups. The angling community is concerned about the loss of fishing opportunities and effectiveness of APAs. Although it is still unclear whether establishment of APAs alone can effectively protect aquatic resources, actively including the recreational angling community in the design, implementation, and management of APAs will help ensure the values of this rather substantial user group are incorporated into aquatic conservation strategies. Conversely, the probability of increasing the sustainability of recreational angling and related economies will be greatest if recreational angler groups remain open minded to both short-term and long-term goals of fisheries conservation strategies, including the use of APAs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".