Evaluation of bus-route and aerial-access methods for Great Lakes recreational fisheries surveys
Bibliographic record
Abstract
Management agencies in the Great Lakes region invest heavily in recreational fisheries surveys to achieve sustainable fisheries management. Costly aerial surveys are often utilized to survey expansive and complex recreational fisheries in the Great Lakes. Using concurrent field surveys and Monte Carlo simulations, bus-route and aerial-access creel survey methods were evaluated and compared for the recreational fisheries of the Michigan waters of Lake Erie. The two methods yielded comparable estimates for the 2021 surveys. The bus-route method was more cost-effective than the aerial-access method. The simulations show that the bus-route method produced unbiased estimates and was statistically more efficient than the aerial-access method. The simulations indicate that the aerial count method was subject to an often overlooked undercoverage bias due to its inability to cover nighttime fishing. Simulations allowed for the identification of estimators with relatively small bias under certain conditions for the aerial count method. Overall, the bus-route method was found to be statistically sound and cost-effective for surveying the Lake Erie recreational fisheries.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".