Assessing Driving with the Global Positioning System: Effect of Differential Correction
Bibliographic record
Abstract
The Global Positioning System (GPS) offers new opportunities for the assessment of driving. GPS receivers acquire signals from a constellation of satellites to determine position. By logging position and time data, other kinematic parameters, such as velocity and acceleration, can be derived. Until recently, errors were intentionally introduced to reduce positional accuracy. Without the intentional degradation, it may be possible to use uncorrected data for assessment of driving. Differential correction can be performed by using a reference station to correct errors. GPS was used to determine the position, velocity, and acceleration of a vehicle. The main objectives were to compare corrected and uncorrected positions, velocities, and accelerations. Bland–Altman plots showed about two times as much error in the north–south position or velocity than in the east–west position or velocity when uncorrected data were compared with corrected data. Acceleration data showed no systematic differences between corrected and uncorrected data. Most errors between data sets could be explained by satellite geometry, transitions in the satellites being used, and the direction of travel. The greatest effect of differential correction was on position. Most uncorrected velocity and acceleration data would be acceptable for the assessment of driving. However, if positional information is needed, then correction should be done.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".