Effectiveness of fluorescent angling lures on catch outcomes in European perch ( <i>Perca fluviatilis</i> ) under ambient light conditions
Bibliographic record
Abstract
Anecdotal reports in angling media suggest that using fluorescent lures may increase catch rates in dim light or at high turbidity. We conducted a controlled angling experiment, comprising 501 30-min experimental fishing trials in three meso- to eutrophic waterbodies and assessed catch rates and sizes of European perch ( Perca fluviatilis) caught when offered two soft plastic lures (fluorescent vs. nonfluorescent) with similar reflective spectra. We also examined fluorescent properties of a range of market-available lures and modeled the experimental lure’s fluorescing effects under natural lake light. Considering the specific light environment of the study waters, the experimental fluorescent lure could get excited by downwelling visible daylight and fluoresce at depths of up to 3 m. Based on a sample catch of 331 perch, and after controlling for interactions with illuminance, cloud cover, water depth and daytime, the fluorescence of the experimental lure did, however, neither affect the catch rate nor the size of perch caught. Lure fluorescence maybe less important than many anglers believe, but further studies in different lake conditions are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".