Leveraging Earth Observation Data to Monitor Boat‐Based Recreational Fishing
Bibliographic record
Abstract
ABSTRACT Recreational fishing is widespread in coastal zones and exerts significant ecological, fisheries‐related and socio‐economic pressures. Unlike commercial fishing, small‐scale recreational fleets are challenging to monitor because they lack enforced use of vessel tracking systems such as the Automatic Identification System (AIS). Recently, remote sensing technologies have emerged as promising alternatives for monitoring marine activities. Here, we assess the potential of high spatio‐temporal resolution satellite imagery to monitor daily changes in recreational fishing boats during a temporal fishing ban within a marine protected area. By comparing satellite‐derived boat detections with AIS records, we demonstrate that satellite data can reliably capture daily changes in recreational fishing activity missed by AIS, including a marked increase immediately following the end of the ban. These findings confirm that satellite observations can consistently detect small fishing boats and reveal their fine‐scale spatio‐temporal patterns. When complemented with local knowledge, this approach enhances our capacity to contribute to the spatial planning and ecosystem‐based management of recreational 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".