Catch and Non-catch-related Determinants of Where Anglers Fish: A Review of Three Decades of Site Choice Research in Recreational Fisheries
Bibliographic record
Abstract
Studies of where people recreationally fish were reviewed to understand which attributes influence these choices, to make this literature accessible to individuals who manage or rely upon recreational fishers, and to shape future research. Between 1988 and 2017, researchers published 114 studies and 189 distinct models of angler behaviors from 96 unique data sets. On average, costs such as travel were universally important while measures of catch-related fishing quality also generally and positively influenced choices of fishing sites. Although frequently omitted from studies, facility quality (e.g., boat launch presence), destination size (e.g., lake area), and measures of environmental quality (e.g., water quality) tended to positively influence choices of fishing sites by anglers. Finally, the influence of regulations and congestion on fishing site choices was more often a significant factor in the choice of hypothetical (i.e. stated preference) than actual (i.e. revealed preference) fishing trips. Researchers are also encouraged to facilitate future reviews by: (i) more clearly communicating details of their studies; (ii) enhancing comparability among studies by using where possible standardized attribute measures; (iii) explicitly testing alternate model specifications related to how anglers’ tradeoff fishing site attributes and; (iv) expanding the scope and scale of research on where people fish.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".