Operational Use of RADARSAT SAR Data as Aid to Winter Navigation in the Baltic Sea
Bibliographic record
Abstract
RÉSUMÉLe Service des glaces de l'Institut finlandais de recherche marine a utilisé des données RADARSAT dans ses opérations de routine au cours de l'hiver 1997/98. Plus de cent scènes en faisceau étroit ScanSAR ont été acquises conjointement par les Services finlandais et suédois des glaces et des services de gestion opérationnelle de brises glaces. Les scènes ont été reçues par la Station satellitaire de Tromsø, en Norvège, et relayées au Service des glaces par transmission ftp dans un délai de deux heures. La première scène a été reçue le 8 février. Les scènes ont été corrigées et utilisées par le Service des glaces dans ses routines quotidiennes de même que pour le développement d'un algorithme de classification automatisé. Un certain nombre de scènes ont été compressées et transférées aux brises glaces et, à titre expérimental, à deux navires marchands en route pour Saint-Pétersbourg, en Russie. Les scènes ont été jugées très utiles à la fois par le Service des glaces et par les autres utilisateurs.SUMMARYThe Ice Service of the Finnish Institute of Marine Research made use of RADARSAT data in its operational routines in the winter of 1997/98. A total of one hundred screens of ScanSAR narrow data was bought jointly by the Finnish and Swedish Ice Services and icebreaker operational managements. The screens were received by the Tromsø Satellite Station, Norway, and retrieved by the Ice Service as an ftp-transmission two hours later. The first screen was received on February 8th. The screens were corrected and used by the Ice Service in its daily routines as well as in the development of an automated classification algorithm. A selection of screens was compressed and transferred to the icebreakers and, by way of test, to two merchant ships engaged on voyages to St. Petersburg, Russia. The screens were regarded as highly useful both by the Ice Service and by the other end-users. Additional informationNotes on contributorsJ. Vainio• Jouni Vainio, Markku Similä and Hannu Grönvall are with the Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, FINLAND, Phone: +358-9-613941, Fax: +358-9-6139-4494 E-mail: jouni.vainio@fimr.fi, markku.simila@fimr.fi, hannu.gronvall@fimr.fiM. Similä• Jouni Vainio, Markku Similä and Hannu Grönvall are with the Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, FINLAND, Phone: +358-9-613941, Fax: +358-9-6139-4494 E-mail: jouni.vainio@fimr.fi, markku.simila@fimr.fi, hannu.gronvall@fimr.fiH. Grönvall• Jouni Vainio, Markku Similä and Hannu Grönvall are with the Finnish Institute of Marine Research, P.O. Box 33, FIN-00931 Helsinki, FINLAND, Phone: +358-9-613941, Fax: +358-9-6139-4494 E-mail: jouni.vainio@fimr.fi, markku.simila@fimr.fi, hannu.gronvall@fimr.fi
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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".