Airborne Ice Thickness Measurement System - Opportunities and Impacts
Bibliographic record
Abstract
Abstract In ice frequented regions, the potential for large ice floes and extreme ice features encroaching on offshore structures can be significant. An early warning system is desired to discriminate between thin ice of no risk and thick ice with significant challenge. The severity and variability of ice conditions will affect the feasibility of operating in such a region, with significant impact on the design and selection of resources to be used and the ice management requirements to support exploration and development. By measuring the ice thickness, operators can determine the operational risk for ice management operations. In addition, it can help map the areas of thin ice to aid shipping route selection. Despite its fundamental importance, sea ice thickness is one of the most difficult measurements to obtain via remote sensing. Passive remote sensing methods at the near infrared, thermal infrared and visible electromagnetic wavelengths, are restricted due to fog, precipitation, clouds, and Polar darkness. Thus active sensing techniques are deemed to be the only feasible method of measuring ice thickness, especially if they can be mounted in aircraft or satellites. Technical solutions are available to measure the thickness of sea ice, but they do not provide a physical measurement over a wide swath of ice. Thus, the authors are developing a wide swath ice thickness measurement system to fill this gap. The most practical solution for ice thickness measurement is an airborne radar. The authors have completed the preliminary design of a system that will combine an ice penetrating radar with a microwave synthetic aperture radar (SAR). The penetrating radar will be used to glean physical measurements of ice thickness, to be fused with the wide swath SAR to produce an ice thickness map.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".