Aircraft Anti-Icing Fluids Endurance Under Natural and Artificial Snow: a Comparative Study
Bibliographic record
Abstract
The usage of De-Icing and Anti-Icing fluids is the most common method recognized to protect aircraft on the ground from freezing and frozen contaminants. The snow endurance times, which means the duration that a fluid can protect the vehicle from snow accumulations, is currently determined outdoor under natural conditions. To replace this expensive and very impractical method, the Anti-Icing Materials International Laboratory developed snow machine was used to perform a comparative study. In this first study, three commercial fluids were tested at various snow intensity rates under artificial snow generated with the snow machine in a cold chamber to validate the testing procedure and investigate the way forward. The results obtained were then positively compared to natural snow endurance times. They were also compared with natural snow regression curves, showing similar trends. This study demonstrated the great potential of this method showing the necessity of pursuing this investigation.
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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".