Characterization of Hazardous Ice using Spaceborne SAR and Ice Profiling Sonar: Preliminary Results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Ice can pose hazard for operations (e.g., transportation, shipping, offshore oil and gas exploration) and for infrastructure (e.g., ports, pipelines, offshore structures). There is an increasing need for fine scale characterization of hazardous ice conditions. This information is of interest to many stakeholders including government departments and agencies, and the oil and gas industry. Spaceborne Synthetic Aperture Radar (SAR) sensors have demonstrated the viability and cost-effectiveness of near-real-time monitoring of the regional ice conditions. Satellite derived ice information products typically rely on the interpretation of ice analysts or in some cases semi-automated techniques, and cover relatively large areas at coarse resolution. Development of improved data products using high spatial resolution polarimetric RADARSAT-2 datasets (e.g., Fine Quad) is desired for detailed characterization of potentially hazardous ice conditions. Although validation of ice data products is challenging due to limited ground truth data, there are numerous sites throughout the Arctic with many years of continuous measurements of ice conditions obtained using bottom mounted Upward Looking Sonar (ULS) instruments. Using ULS data we have recently developed analytical methods to characterize highly deformed sea ice features including large individual keels, segments of hummocky ice, multi-year ice, and episodes of internal ice stress, which can also serve as validation data for SAR-based analysis. This paper presents an overview of our ongoing work and very preliminary results on hazardous ice characterization using SAR and ULS data. ULS data view from below and SAR data view from above are complementary information sources, and utilizing both is expected to result in better characterization of the ice conditions. During this work, paired SAR and ULS datasets will be generated to allow calibration and validation of algorithms, and methodologies will be developed to utilize these complementary data sources. This project is expected to (1) develop improved methods for fine scale analysis of RADARSAT-2 data; (2) develop enhanced information products generated in the hindcast mode when ULS and RADARSAT-2 are both available; (3) demonstrate potential for RCM (compact polarimetry). Calibrated and validated information products of hazardous ice will be extremely valuable for users who require such information for engineering design, to make management and policy decisions, and to safely perform operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it