Identifying recreational fisheries in the Mediterranean Sea through social media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The impact of recreational fishing on fish stocks remains largely unknown, as this is inherently difficult to monitor, especially in areas such as the Mediterranean Sea where many species are targeted using a variety of fishing gears and techniques. This study attempts to complement existing data sets and construct the profile of recreational fisheries in the EU ‐Mediterranean countries using videos publicly available on social media. A total of 1526 video records were selected, featuring the capture of 7799 fish specimens. The results show recreational fishing is multispecies in nature (26 species contributed to >80% % of the most numerically important species caught) and exhibits a spatially homogeneous pattern, with differences in species composition being mostly dependent on the fishing technique used rather than on the country. Such findings fill an important knowledge gap on recreational fishing activities, and the methodology provides an innovative approach to gather statistics on data‐poor thematic areas that can potentially complement other data sets, such as the EU Data Collection Multi‐Annual Programme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it