Bibliographic record
Abstract
Friction measurement can be determined readily and accurately using modern technology. Timeliness, not accuracy, is considered a problem in regard to ground vehicle and aircraft operation friction measurement, since weather conditions can change pavement surface friction in a matter of minutes. It is therefore very important to give pilots actual friction measurement values, as well as times of measurement. The author describes different facets of runway friction and its measurement, as well as the Joint Winter Runway Friction Measurement Program (JWRFMP). JWRFMP is a joint project of NASA Langley Research Center, the Federal Aviation Administration, Transport Canada, and several Asian and European aviation authorities and organizations. Its primary purpose is conducting research with the aim of minimizing hazardous runway accidents. JWRFMP developed techniques and standards have significantly reduced ground friction measuring device variance, making it possible for the common scale developed for all devices and aircraft braking performance to agree. Agreement between aircraft braking performance and one of the indices should be established and enhanced through future work. The author argues that there will be minimization of aircraft accidents due to friction loss and improvement in safety of aircraft operations in adverse weather conditions through collection and use of actual friction numbers from properly calibrated ground vehicles. This would require less concern over possible litigation and more encouragement from regulatory agencies.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".