Ice Information Services in the Era of Multiple SAR Satellites and Advanced Numerical Models
Bibliographic record
Abstract
Abstract Marine ice represents one of the greatest hazards to offshore developmentand transportation. While some operations may not tolerate the presence of anyice; others may only be affected by the most significant ice features such asmultiyear, heavily deformed sea ice or ice islands. With multiple SyntheticAperture Radar (SAR) and optical satellites, including RADARSAT-1 andRADARSAT-2, Envisat, Terra SAR X, NOAA NPP, etc., it is possible to have atleast daily coverage of virtually all areas of the Arctic. Ice informationservices have not only increased the accuracy of their products, but they havealso expanded the products available to include very detailed image analysesand tracking of specific ice hazards. At the same time, significant efforts inthe fields of data assimilation and modeling have started producing numericalsea ice analysis and forecast guidance products as part of routine operations. Techniques are currently under development in the field of ensemble modeling. These developments will introduce probabilistic forecast products that willprovide both improved ice information and measures of forecast confidence. These directions will enable the offshore development and transportationindustries to expand their risk management techniques to include ice andenvironmental information services. This presentation will provide a review ofthe systems under development Background Current ice information services provide sufficient guidance for a widevariety of marine operations to function safely. The complexity of theinformation required varies with the experience and use of the clients. Icebulletins warn of current and short term hazardous ice conditions. Daily icecharts suit the need of many users although more frequent coverage may berequired for optimal navigation. More advanced users may requiresatellite image analyses or even raw satellite images. The latter combined withshipboard marine radar and an experienced ice pilot will allow for an efficientevaluation of current and very short range ice conditions. Information on thelimitations of individual products and their day to day variations in accuracycould certainly improve their usability. Even less information is available onhow conditions will evolve even beyond 24 hours. Products are sometimesavailable on a predefined area and resolution in a limited number of productformats. Electronic charting may help in some ways but the amount ofinformation available to clients is quite limited and expensive to prepare.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".