EVT-SIAM: A Tool Based on Extreme-Value Theory for the Assessment of SBAS Accuracy and Integrity
Bibliographic record
Abstract
Following the start of WAAS extensions in Canada, in Alaska and in Mexico, as well as the start of WAAS system evolutions between 2005-2008, and considering the noticed improvements of performances and procedures, the FAA suggested widening the WAAS services up to LPV200 (aka. category I approaches), with a vertical alert limit (VAL) of 35m. This objective has also been assigned to EGNOS in Europe, and ICAO Annex 10, Volume 1 has been amended in that direction (Amendment #85). Though most of the specifications of LPV200 are identical to those for APV1 service level (where VAL = 50m) in terms of integrity, horizontal alert limit, availability and continuity, different or additional specifications have been introduced in the field of positioning accuracy. Indeed, the specification of APV1 accuracy – that the 95th centile of the horizontal error (H-NSE) be below 16 m, and the 95th centile of the vertical error (V-NSE) be below 20 m – becomes for LPV200 in the vertical domain that - V-NSE_95 % 10m) 15m) VAL or HPE > HAL) < 2E-7 per 150 s where VAL (resp. HAL) the vertical (resp. horizontal) alert limits values are fixed by the International Civil Aviation Organisation for different flight phases. The failures to be taken into account are the ones that affect the used basic constellations and GNSS augmentation systems. This latter probability results from an allocation that takes into account the probability that a given failure occurs and of the probability of detection. Verifying such low probabilities with standard statistical methods require several months or years of observation data, even with a 1-second accuracy sampling. This is not realistic and compatible with industrial constraints that require system qualification time scales. For instance, such rare “events” are generally not observed within the data used to qualify a SBAS release because of their scarcity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".