Assessment of Driving With the Global Positioning System and Video Technology in Young, Middle-Aged, and Older Drivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Driving is a complex task that is difficult to fully characterize objectively or in a blinded fashion. The main objective of this study was to determine the usefulness of the global positioning system (GPS) and video technology for examining age-related differences in driving. In this study, GPS was used to determine the position, velocity, and acceleration of a vehicle, driven by subjects of different ages, while video footage was used to provide a detailed context of the drive. METHODS: Twenty-four subjects who were young (20 to 29; n = 6), middle-aged (30 to 64; n = 8), and older (65 years of age and older; n = 10) drove their own vehicles on a 30-km route of various types of roads, with a GPS receiver and video camera recording. RESULTS: The combination of GPS and video data allowed for the determination of many age-related driving differences. The young subjects drove faster, had a shorter deceleration distance and time, as well as a shorter acceleration time. Young subjects also had a substantially higher number of infraction demerit points primarily due to speeding, not stopping fully at stop signs, and following too closely. Although the older subjects had a smaller number of demerit points assessed, they tended to make different types of errors than the young subjects, including not stopping at all at a stop sign and turning errors. CONCLUSIONS: GPS and video technology offer new opportunities for the assessment of age-related driving performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it