Expert opinion on using angler Smartphone apps to inform marine fisheries management: status, prospects, and needs
Bibliographic record
Abstract
Abstract Smartphone applications (apps) that target recreational fishers are growing in abundance. These apps have the potential to provide data useful for management of recreational fisheries. We surveyed expert opinion in 20, mostly European, countries to assess the current and future status of app use in marine recreational fisheries. The survey revealed that a few countries already use app data to support existing data collection, and that this number is likely to increase within 5–10 years. The strongest barriers to use app data were a scarcity of useful apps and concern over data quality, especially biases due to the opt-in nature of app use. Experts generally agreed that apps were unlikely to be a “stand-alone” method, at least in the short term, but could be of immediate use as a novel approach to collect supporting data such as, fisheries-specific temporal and spatial distributions of fishing effort, and aspects of fisher behaviour. This survey highlighted the growing interest in app data among researchers and managers, but also the need for government agencies and other managers/researchers to coordinate their efforts with the support of survey statisticians to develop and assess apps in ways that will ensure standardisation, data quality, and utility.
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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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".