Comparative analyses with conventional surveys reveal the potential for an angler app to contribute to recreational fisheries monitoring
Bibliographic record
Abstract
Growing interest in apps to collect recreational-fisheries data requires that relationships between self-reported data and other fisheries data are evaluated, and that potential biases are assessed. This study compared results from a mobile-phone application and website for anglers (MyCatch) to results from three types of fisheries surveys — 1 provincial-level mail survey, 2 creel, and 17 gillnet surveys. Results suggest that an app and website can (i) recruit users that have a broad spatial distribution that is similar to conventional surveys, (ii) generate data that capture regional fishing patterns (2218 trips on 289 lakes and 90 streams or rivers), and (iii) provide catch rate estimates that are similar to those from other fisheries-dependent surveys. Some potential biases in app users (e.g., urban bias) and in the relative composition of species caught provincially were identified. The app was not a suitable tool for estimating fish abundance and relative community composition. Our study demonstrates how apps can or cannot provide a complementary data-collection tool for recreational-fisheries monitoring, but further research is needed to determine the applicability of our findings to other fisheries contexts.
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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 | 0.019 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".