Optimizing Creel Surveys
Bibliographic record
Abstract
Abstract The standardization of monitoring protocols across jurisdictions is a common theme in fisheries management because it provides a straightforward method for unbiased comparisons among different systems (i.e., across watersheds, lakes, states, or countries) and through time. However, in fisheries, one size rarely fits all, and as such, standard protocols must sometimes be optimized to fit the conditions of the study or management priorities. Creel surveys (i.e., the collection of socio-economic, fish, and fisheries information related to fishing trips and activities) are no exception. Further, over a long enough time, a creel survey may need modifications as the fish stock or societal conditions surrounding the fishery change. This chapter offers advice on how to measure the effectiveness of traditional creel survey protocols (e.g., stratified single-system angler interview and count surveys) and incorporate newer methods to gain additional insight into creel survey design and angler behaviour. First, we offer some practical advice on linking the mandate of fishery management with the outputs of creel surveys. Second, we introduce some approaches to improving the efficiency of the creel survey if the analyst deems the general protocol unbiased. Third, we present some approaches to identify, mitigate, and quantitatively correct biased data. Finally, we discuss how a Bayesian-based joint estimation modelling framework can combine multiple survey protocols (e.g., roving and waterbody access point, vehicle counts, and aerial angler counts) into one creel survey design.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".