Evaluation of a citizen science platform for collecting fisheries data from coastal sea trout anglers
Bibliographic record
Abstract
There are often limited data available to support the sustainable management of recreational fisheries. Electronic citizen science platforms (e.g., smartphone applications) offer a cost-effective alternative to traditional survey methods — but these data must be validated. We compared sea trout (Salmo trutta) data from a Danish citizen science platform with three independent traditional surveys: a roving creel survey, an aerial survey, and a recall survey. The comparisons include fisheries data (e.g., catch, release, effort, and fish size structure) and demographic descriptors (e.g., age) that were collected within the same spatial and temporal frame. We found general alignment between recreational sea trout catch and effort data that were provided by citizen scientists, or collected by more traditional survey methods. Our results demonstrate that citizen science data have the potential to supplement traditional surveys, or act as an alternative source of catch and effort data. However, results were from a highly specialized fishery within a limited spatial and temporal frame, so more research is needed to assess their relevance over time and to a broader set of 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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".