Efficacy and feasibility of a breath sensor for detecting driver fatigue and drowsiness
Bibliographic record
Abstract
Efficacy and feasibility of a breath sensor for detecting driver fatigue and drowsinessCarl I. Schulman, Chitvan Killawala, Umer Bakali, Jeramy Baum, Emre Dikici, Kevin Miller, Kelly Withum, Sapna Deo, Leonidas Bachas and Sylvia DaunertExploring disparities in cannabis-impaired driving: a sociodemographic and behavioural analysis based on the Canadian Automobile Association (CAA) surveys in Ontario, 2021–2023Renzo Calderón Anyosa, Robert Colonna, Christine M. Wickens, Michael Stewart and Brice BatomenInflammatory protein elicitation in response to whole-body vibration exposureNicholas Miller, Suzanne Konz, Steven Leigh and Holly CyphertMotor vehicle crash occupants with tibial fracture have different outcomes based on patient zip codeDanelson K. A., Cooper A., Gwam C., Reiser J., Henry K. and Pilson H.Pedal confusion, pedal errors, and consequent unintended accelerations in drivers with diabetic peripheral neuropathyM. Esselaar, M. Perazzolo and D. E. Marple-HorvatInvestigating the determinants of over-speeding behavior among car drivers in India using theory of planned behavior and psychological flow theoryHarshita Joshi and Ankit Kumar YadavRisk factor for serious injury of far-side occupants in motor vehicle side crashes using the KIDAS (Korean In-Depth Accident Study) dataChan Young Kang, Kang Hyun Lee, Oh Hyun Kim, Yeon Il Choo, Dooruh Choi, Dong Gu Kang, Jin Ho Yu, Jung Hun Lee Hee Jin Kim and Hee Young Lee
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".