Aircraft takeoff performance monitoring in far-northern regions: An application of the global positioning system
Bibliographic record
Abstract
A design approach for an aircraft takeoff performance monitoring system (TOPMS) is described. In this approach, it is proposed that the Global Positioning System (GPS) in conjunction with a discrete Kalman Filter be used to determine aircraft acceleration, ground speed, and position relative to the end of the runway. A practical evaluation of the feasibility of this proposal showed clear superiority of a GPS-derived acceleration over a more traditional method employing accelerometers. This study found that, when compared to observations from carefully mounted accelerometers, the GPS-derived observation agreed to within 0.10 metres per second squared ninety percent of the time. Advantages of the GPS-derived observation included a modest noise level, insusceptibility to gravity and temperature-influenced variations, and far simplified mounting criteria. A theoretical dynamic model of an aircraft in contact with the ground was developed in consideration of factors pertaining to runways at far-northern Canadian airports. In the model, factors such as runway slope, wind velocity, wheel friction coefficient, and aircraft control settings were considered constant. While variability in any parameter considered constant by the model could influence the performance of a TOPMS, such variability was deemed beyond the scope of this preliminary investigation of a TOPMS designed specifically for the far-northern environment. A device containing a GPS receiver and data acquisition system was designed and certified, then installed in an aircraft operated by an airline servicing far-northern Canadian airports. The data collected in this manner were used to validate the theoretical model. It was concluded that a projection of displacement can be determined to within an uncertainty of fifteen metres in sufficient time to alert the pilot of an unsafe situation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".