The nexus of fun and nutrition: Recreational fishing is also about food
Bibliographic record
Abstract
Abstract Recreational fishing is a popular activity in aquatic ecosystems around the globe using a variety of gears including rod and line and to a lesser extent handlines, spears, bow and arrow, traps and nets. Similar to the propensity to engage in voluntary catch‐and‐release, the propensity to harvest fishes strongly varies among cultures, locations, species and fisheries. There is a misconception that because recreational fishing happens during non‐work (i.e. leisure) time, the nutritional motivation is negligible; therefore, the role of recreational fishing in supporting nutrition (and thus food security) at regional, national or global scales is underappreciated. We consider the factors that influence whether fish will be harvested or released by examining the motives that underlie recreational fishing. Next, we provide an overview of the magnitude and role of recreational fishing harvest in supporting nutrition using regional case‐studies. Then, we address issues such as contaminants and parasites that constrain the ability of fish harvested by recreational fishers to be consumed. Although recreational fishing is foremost a leisure activity, the harvest of fish for personal consumption by recreational fishers has contributed and will continue to contribute to human nutrition by providing an accessible, affordable and generally highly sustainable food source, notwithstanding concerns about food safety and possibly overfishing. Attempts to better quantify the role of fish harvested by recreational fishers and the relative contribution to overall food security and personal nutrition will provide resource managers and policymakers the information needed to guide management activities and policy development.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".