Bibliographic record
Abstract
Ottawa, Canada, where he is involved in data collection and flight trials in support!of GPS applications for aircraft. Mr. Graham holds an engineering degree from the University of Ottawa, a commercial pilot’s licence and an instrument rating. The safety benefits of approaches with vertical guidance are well recognised, but this level of service is typically available only at major airports (with ILS) or to aircraft with sophisticated and costly avionics (Baro VNAV). In May 2000, when selective availability was set to zero, GPS accuracy increased significantly. Ionospheric effects are now the major source of error, yet even at the height of the sunspot cycle, vertical accuracy in the order of 8.5 m (95%) is being observed. Studies indicate that raw GPS, with suitable monitoring techniques, is able to meet the certification requirements for VNAV equipment, and in fact outperforms currently certified barometric VNAV systems in terms of accuracy and integrity. Newer TSO C129a panel mount receivers are being designed with analogue hardware to support!VNAV, and the aviation databases already accommodate the parameters required to define a vertical path. This paper investigates the concept of using raw GPS altitude data to provide vertical guidance, with integrity, on LNAV-only and LNAV/VNAV approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.250 | 0.105 |
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".