The nexus of fun and nutrition: Recreational fishing is also about food
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
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.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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