Validation of RADARSAT-1 vessel signatures with AISLive data
Bibliographic record
Abstract
AbstractAutomatic identification system (AIS) data have the potential to contribute significantly to the development of automated algorithms for ship signature identification in remotely sensed imagery. Datasets composed of RADARSAT-1 imagery collected over a 3-month period and the corresponding AIS data from AISLive were compiled. SAR-derived ship length and radar cross section features were validated against AIS data, thus demonstrating the utility of AIS as a source of ground truth.Les données du système d'identification automatique (AIS) peuvent contribuer de façon significative au développement d'algorithmes automatisés pour l'identification des signatures de navire dans les images de télédétection. Des ensembles de données composés d'images RADARSAT-1 acquises au cours d'une période de 3 mois et les données correspondantes du système AIS de AISLive ont été compilées. Les longueurs de navire dérivées des données RSO et les caractéristiques de surface équivalente radar ont été validées par rapport aux données AIS, démontrant ainsi l'utilité des données AIS comme source de réalité de terrain.[Traduit par la Rédaction]
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".