A remote ice detection system suitable for marine and aerospace applications
Bibliographic record
Abstract
A new instrument for remote ice detection and thickness measurement is described. It incorporates two optically based technologies that give it capability to measure the thickness of clear or foggy layers of solid or liquid on surfaces. The prototype device, known as RIDE (Remote Ice Detection Equipment), is capable of measurements on moving surfaces such as wind turbines, and aircraft propellers and rotors. The current RIDE prototype is intended for relatively long distance measurements however a small, and much simpler, version of the technology is planned for short distance measurements (within a few meters). In the marine context a miniaturized version of the device could be utilized to regulate power for heating elements intended to deice walkways and stairways on vessels and structures. Similarly it could serve as a warning system to signal the encumbrance of safety and communications equipment. In the aerospace context a miniaturized device could measure and signal the need for mitigating strategies when icing accumulates on components of manned and drone-type aircraft. Details of the RIDE prototype, and miniaturization plans, are presented along with thickness data acquired during two test programs, one study on clear ice layers where measurements were obtained from a distance of ~ 15 m and the other on foggy ice layers at a distance of approximately 13 m.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".