Impairment Screening Utilizing Biophysical Measurements and Machine Learning Algorithms
Bibliographic record
Abstract
Drug recognition expert (DRE) officers employ a set of tests to investigate drivers who are under impairment and to determine the type of drug that they have used. Horizontal Gaze Nystagmus (HGN), Walk and Turn (WAT), and One Leg Stand (OLS) are the main three tests included in the Standardized Field Sobriety Tests (SFSTs), which lead the officers to evaluate the sobriety of drivers. Performing these tests requires trained officers, but the final decision may still be subjective. These tests along with a suite of comprehensive (yet manual) at-station testing are the basis of police decision making and are subjected to scrutiny by courts. Therefore, designing an automated system to detect impairment not only will help officers in making accurate decisions, but also will remove the subjectivity and can potentially serve as a court-admissible evidence. In this paper, a new method for automated impairment detection is introduced and implemented using data analysis and machine learning algorithms based on a comprehensive suite of tests performed on 34 participants.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".