A Constellation Of Satellites For Enhanced Mapping Of Sea Ice
Bibliographic record
Abstract
Abstract In conducting safe and cost effective operations in ice prone waters, icemanagement and risk mitigation practices are integral to operations. A criticalelement in ice management is the mapping and characterisation of sea ice. Satellite synthetic aperture radar (SAR) is a standard tool used by icecharting agencies to map the extent of sea ice. Wide-swath SAR has become thepreferred sensor of choice for ice mapping and the collection of data regardingice parameters. SAR provides a high degree of information content on basic iceparameters such as concentration, type and topography. SAR can be used tocharacterise different sea ice types, such as multi-year versus first year ice, and the use of multiple SAR frequencies (L, C and X-Band) can reduceinterpretation ambiguities during the melt season. The advent ofmulti-frequency and polarization SAR systems, acting as a constellation, isseen as an important next step in the evolution of sea ice monitoring. Theevaluation of a SAR ice constellation is an interesting challenge since aquantitative evaluation is necessary. As a consequence, a sea ice backscattertool has been developed that provides a figure of merit estimation of iceclassification from a constellation scenario. The authors have used its sea-icebackscatter tool to simulate various ice constellation scenarios. Thesescenarios will be presented in the context of their utility and versatility inoil and gas operations. The implementation of a SAR ice constellation providesthe opportunity to significantly expand the ice information extractioncapabilities, over and above that of these systems acting alone. In the contextof its use within Arctic resource development, SAR constellations offerenhanced ice charting to the oil and gas industry. Index Terms—Sentinel-1, SAR, sea-ice, backscatter, constellations Introduction In conducting safe and cost effective operations in ice prone waters, icemanagement and risk mitigation practices are integral to operations. A criticalelement in ice management is the mapping and characterisation of sea ice. Satellite synthetic aperture radar (SAR) is a standard tool used by icecharting agencies to map the extent of sea ice. Wide-swath SAR has become thepreferred sensor of choice for ice mapping and the collection of data regardingice parameters. SAR provides day-night, all-weather capability and relativelyhigh resolution. Additionally, SAR can provide a high degree of informationcontent on basic ice parameters such as concentration, type and topography.
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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".