North American Journal of Fisheries Management 23:573–580, 2003 q Copyright by the American Fisheries Society 2003 Exaggeration of Walleye Catches by Alberta Anglers
Bibliographic record
Abstract
Abstract.—I studied anglers ’ exaggeration of catches of walleyes Stizostedion vitreum at Alberta sport fisheries to determine whether trends in reported catches were indicative of actual trends. To quantify anglers ’ exaggeration, I compared the ratios of protected-length to legal-length walleyes as reported by anglers with similar ratios confirmed from test angling at 22 walleye sport fisheries from 1991 to 2000. Overall, anglers reported catching 2.2 times more protected-length walleyes per legal-length walleyes than were caught in the test-angling fisheries. Exaggeration in catches was not constant but increased exponentially with decreasing catch rate. On-site exaggeration, in combination with further exaggeration in mail surveys, results in the reported catch rate declining at a lower rate than the actual catch rate, thereby causing a perception of hyperstability in the fishery. Hyperstability has profound implications for biologists who manage fisheries based on reported data because reported catch rates may provide little warning of a fisheries collapse. Many sport fisheries are managed using catch-and-release or length-limit regulations, so fisheries managers must increasingly rely on anglers ’ re-ports of fish catches as a monitoring tool rather
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".