Effectively managing angler satisfaction in recreational fisheries requires understanding the fish species and the anglers
Bibliographic record
Abstract
Whenever satisfied anglers are an important objective of recreational fisheries management, understanding how trip outcomes influence satisfaction reports is critical. While anglers, generally, prefer high catch rates and large fish, the relative importance of these catch outcomes for catch satisfaction has not been established across species and angler types. We examined relationships between angler specialization, trip outcomes (both catch and non-catch characteristics such as crowding), and catch satisfaction across six freshwater fish species in northern Germany. As expected, catch satisfaction was primarily determined by catch rate and fish size in all fish species; however, the relative importance of these two outcomes varied considerably across species and among angler types that differed by commitment to fishing. We found a diminishing marginal return of satisfaction for increasing catch rate for all but small-bodied cyprinid species, while increasing size of largest retained fish monotonically increased catch satisfaction in all species we examined. Non-catch outcomes (e.g., the number of other anglers seen while fishing) also had a significant negative influence on catch satisfaction, suggesting that non-catch factors are important in establishing expectations and for contextual evaluation of catch outcomes. We also determined that diversified trips made anglers more satisfied and that all else being equal, specialized anglers increased catch satisfaction from travel and fishing time. The results highlight the importance for managers to consider their particular mix of anglers as well as the fish species present when setting regulations aimed at increasing angler satisfaction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".