Fisheries enforcement through vessel localization using AVHRR and SAR technology
Bibliographic record
Abstract
Natural Resources Consultants, Western Resources Analysis and NASA are investigating the application of Advanced Very High Resolution Radiometer (AVHRR) and Synthetic Aperture Radar (SAR) technology to fisheries enforcement concerns. The goal of the project is to develop and market a surveillance system that can passively monitor fishing vessel locations in near real time. Work has been conducted along the US West Coast and in the Bering Sea. Vessel tracks were observed with regularity in the West coast AVHRR data but were less detectable in the more complex meteorological conditions found in the Bering Sea. ERS-1 SAR data did reveal 50 m to 100 m fishing vessels operating on fishing grounds in the Bering Sea. The larger vessels were detected despite wind-roughened seas and the presence of speckle. The smaller vessels also had good returns and both vessel classes may be detectable with automated methods. Given the usefulness of AVHRR and SAR imagery in detecting vessels, and the potential for near-real time data provision from Canada's RADARSAT, commercialization opportunities are now being investigated.>
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".