Bibliographic record
Abstract
The Joint Winter Runway Friction Measurement Program (JWRFMP) is the focus of this article. An international initiative comprised of more than 30 organizations in 12 countries – including the United States, Canada, France, Germany, Norway, and Japan – JWRFMP extensively tests and measures aircraft performance in terms of braking and runway friction. Because of the critical importance of accurate information about runway and weather conditions, which varies between airports and between countries, JWRFMP has been formed to provide test data that allows for these differences. Through testing, JWRFMP has discovered that braking performance is well correlated with friction, as measured by a decelerometer-type device used in conjunction with winter contaminated runway surface tests. Decelerometers are used only for measuring surfaces contaminated by ice, frost, wet ice, compacted or loose snow, and other phenomena seen during winter operations. The article also discusses the Canadian Runway Friction Index (CFRI), an average of readings taken by the decelerometer that is used to provide pilots with meaningful information about the effect a contaminant has on reducing runway surface friction. Recommended landing distances are also discussed. Derived from models based on CFRI measurements, these development CRFI tables of recommended landing distances are provided here in tabular format.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".