Bibliographic record
Abstract
It has long been recognized that each aircraft responds differently to a pavement surface roughness pattern. On the basis of that observation, the development of two aircraft-based roughness assessment criteria is discussed. Both allow the analysis of pavement surface profiles obtained from surveys carried out at 1-m intervals. The single wavelength roughness criterion looks for acceptable and excessive single double amplitudes in the profiles of runways and taxiways. The overall roughness criterion considers the template and the root-mean-square (RMS) approaches. Both work as a function of the critical wavelengths (L c ) and double amplitudes (2A). These parameters are estimated for each aircraft as a function of its velocity at the takeoff rotational point and the aircraft response frequency. For the template approach, windows are constructed with L c and 2A for each particular aircraft for identifying the acceptable and excessive roughness areas in the profiles. The RMS approach initially estimates RMS limit values for acceptable and excessive roughness for each aircraft on the basis of its critical wavelength. Values of RMS are estimated for runway segments with the same lengths of the critical wavelengths and then compared with the RMS limit values. Excellent agreement was observed between the single wavelength roughness criterion and the FAA and International Civil Aviation Organization criteria. In addition, excessive bump deformations obtained by other authors for Georgia's Hartsfield–Jackson Atlanta International Airport Runway 8L-26R were found to be above the same criterion's excessive roughness curve.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".