Application of the Global Positioning System in determination of vehicular acceleration
Bibliographic record
Abstract
Aircraft landing and takeoff performance monitoring is an area of research aimed at improving the information available to the pilot for decision making during takeoff or landing. A system capable of instantaneously determining the stopping distance of an aircraft could form an integral component of a monitoring system. Particularly difficult to quantify is the frictional coefficient between the runway and the aircraft tires, should such a measurement be necessary. In secluded far-northern regions, where a monitoring system would be particularly useful given adverse weather, few airports are equipped to attempt frictional measurements. In such instances a monitoring system would need to be totally self-contained and able to determine aircraft ground speed, acceleration, and position relative to the end of the runway. Prediction of the aircraft's location at rest is then possible. It is proposed that the Global Positioning System (GPS) 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. Advantages of the GPS-derived measurement include a modest noise level, insusceptibility to gravity and temperature-influenced variations, and far simplified mounting criteria.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".