Assessment of Runway Surface Conditions by British Pendulum Testing under the Global Reporting Format Winter Conditions
Bibliographic record
Abstract
Poor braking performance on runways during winter is one of the most significant factors causing runway excursions. The International Civil Aviation Organization (ICAO) proposed a standardized method, named the Global Reporting Format (GRF), to establish runway surface conditions based on human observers. The GRF includes the description of most winter conditions. This work aimed at reproducing the GRF winter conditions in the laboratory, and to monitor the effect of the surface conditions, both without surface treatment and with surface treatment using liquid runway de-icing products (RDP) in both de-icing and anti-icing modes, using the British Pendulum Tester (BPT). The reproduction of the GRF winter conditions was possible in the laboratory since the BPT results correlated well with the GRF ratings. The worst conditions retained snow on ice. Wet conditions obtained with water and RDPs (potassium formate KFO, potassium acetate KAC and hybrid glycol-KAC HYB) behave similarly, indicating that RDPs alone on a runway for prevention are not any more dangerous than just water. The range from wet to dry, equivalent to a British Pendulum Number (BPN) from ~40 to ~50, was considered as good conditions on the runway. All the RDPs tested in the study improved the BPN from untreated conditions. In most of the conditions, the RDPs improved the BPN to values in the range of favorable conditions. KFO and KAC reacted almost in the same way for each condition. However, the HYB was better in conditions with ice. The obtained results demonstrated that simulated laboratory winter conditions could be used to determine the efficiency of RDPs in both de-icing and anti-icing modes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".