A One Health perspective on recreational fisheries
Bibliographic record
Abstract
Recreational fisheries involve an intimate connection between people, individual fish, and the environment. Recreational fishers and their health crucially depend on healthy fish and ecosystems. Similarly, fish and ecosystems can be impacted by the activities of people including recreational fishers. Thus, amplified by the global interest in recreational fishing, we posit that recreational fishing is particularly suited as an empirical system to explore a One Health perspective, with a goal of creating pathways to better manage such socio-ecological systems for the benefit of people, fish, and the environment. Although zoonoses are uncommon in fishes, fish can carry pathogens, biotoxins, or contaminants that are harmful to people. When captured and released, fish can experience stress and injuries that may promote pathogen development. Similarly, when humans contribute to environmental degradation, not only are fish impacted but so are the humans that depend on them for nutrition, livelihoods, culture, and well-being. Failure to embrace the One Health perspective for recreational fisheries has the potential to negatively impact the health of fish, fisheries, people, society, and the aquatic environment—especially important since these complex social–ecological systems are undergoing rapid change.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".