Development of a weighted scoring system for the Electronic Driving Observation Schedule (eDOS)
Bibliographic record
Abstract
The electronic Driving Observation Schedule (eDOS) is a novel approach to assessing older drivers' performance in their everyday driving environment on their chosen routes. The original eDOS total score is generated using the count of driving errors, which does not account for distinct risk levels of different types of driving errors made in different complexity of driving environments. This study was conducted to create one score to represent the complexity of driving route during each eDOS observation and one weighted eDOS total score to represent older drivers' performance accounting for the risk of driving errors by their type and the complexity of maneuvers in their corresponding environments. A literature review, a two-round survey with 13 experts in driving evaluation, and iterative discussions between primary investigators were conducted for generating these scores. Two formulae were created to calculate a weighted maneuver/environmental complexity score and a weighted eDOS total score. •An advanced weighted score is created to represent one's on-road driving performance in their everyday driving environment not only using the count of driving errors, but also accounting for the risk level of each error.•The complexity of driving maneuver and environment in each on-road driving trip can be systematically rated.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".