Microwaves See Thin Ice: A Review of Ice and Snow Sensing Using Microwave Techniques
Bibliographic record
Abstract
Ice and snow are a reality that a large percentage of the global population experiences on a regular basis, with more than 31% of the Earth’s landmass[2]experiencing seasonal snow and ice accretion (as shown inFigure 1, a satellite image of the global snow cover for February 2022)[1]. In the United States alone, ice and snow impact 70% of the population, resulting in more than 1,300 annual deaths from icing-related roadway accidents and causing an estimated US$2.3 billion to be spent each year on roadway snow and ice control operations[3]. The infrastructure in regions that receive ice and snow must be specially designed to reliably operate in winter weather conditions, with specific considerations for power grids[4], antenna communication structures, and cable bridges[5]. Expanding marine shipping and industrial operations in arctic regions have increased the need for safe and reliable operation of equipment and ships in atmospheric accretion and salty-icing conditions[6]. Wind turbines with blades rotating at great speeds high up in the air require thorough design considerations for atmospheric ice formation to prevent damage from icing, which can result in substantial power reduction or complete outage[7]. Similarly, ice accretions on flying objects, such as aircraft wings or turbopropellers, are highly critical challenges and have been a focus for sensing and de-icing for decades[8]because of the fatal effects of icing on airplanes[9].
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".