Bibliographic record
Abstract
in the area of receiver hardware and software development and low cost sensor integration. One of the most important issues when using GPS for urban vehicular navigation is the reliability of the position solution, which usually depends on the nature of the environment. In particular, the presence of urban canyons and foliage can cause significant degradation in satellite visibility as well as high multipath. The objective of this paper is to assess the impact of using multiple antennas along with some statistical reliability measure to detect blunders on pseudorange measurements, such that blunders can be rejected before they contaminate the estimated vehicle positions. One of the properties of multipath is that it decorrelates rapidly as a function of distance, so antennas spaced at least 0.5 m apart may be subjected to different multipath conditions making detection possible. The impact of using constraints between various antennas is also addressed. Land tests were conducted with four antenna/receivers in Calgary under various environments including open sky, urban canyon and dense foliage conditions. Results of the multipath blunder detection technique, when applied to the field data, are presented and discussed. An improvement of 10%-40 % in position accuracy was achieved under different conditions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.246 | 0.148 |
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".