Enhancing Winter Runway Safety: A Comprehensive Analysis of Friction Measurement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Worldwide, the aviation industry experienced substantial financial losses of $4 billion in 2019 due to runway excursions. These incidents, notably prevalent during winter, are exacerbated by adverse weather conditions, such as snow, slush, ice, brine, and water, compromising the runway surface. Runway excursions are frequently linked to insufficient braking capabilities, thereby making them a significant contributing factor. An accurate assessment of runway skid resistance is imperative, necessitating the use of a correct testing methodology. However, operators need to help navigate the many measurement devices available worldwide. This review comprehensively analyzed diverse in situ and laboratory skid resistance measuring devices for runway concrete under winter conditions. The apparatuses were classified based on their principle with the associated standard, measurement index, advantages, drawbacks, and specific applications. Some article insights and methodologies are discussed, in which these devices were used to measure skid resistance under winter conditions. Finally, based on different studies, it was determined that the best way to relate the current skid resistance values is by their interfacial condition (dry, wet, or ice), where the highest value of each range represents the dry condition, and zero is the most slippery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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 it