Effectiveness of Enforcement to Deter Illegal Angling Harvest of Northern Pike in Alberta
Bibliographic record
Abstract
Abstract We studied anglers' perceptions of an enhanced enforcement strategy and the effects of this strategy on the illegal harvest of northern pike Esox lucius at recreational fisheries in Alberta. The strategy was designed by Alberta enforcement officers and consisted of varying patrol frequency and strongly worded warning posters. Monitoring effects of this strategy at nine popular Alberta lakes during 2001 and 2002 showed that intensive patrol events did not change anglers' perceptions of enforcement. Anglers' perceptions of detection (i.e., certainty of punishment) increased with enforcement effort, but not with the use of warning posters. Anglers' perceptions of penalties (i.e., severity of punishment) increased with the use of signs, but not with increased patrol effort. We observed a tendency toward reduced illegal harvest at lakes where anglers perceived high deterrence (defined as the product of certainty and severity of punishment), although anglers consistently overestimated the actual risks of detection. Anglers perceived that the chance of detection increased as enforcement effort increased, with an asymptotic maximum perception when officers contacted more than 3% of anglers. These results suggest that officer efficiency in deterring anglers' illegal behavior at these lakes is optimized by applying no more than this level of enforcement effort.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".