Pilot Test of Fatigue Management Technologies
Bibliographic record
Abstract
This study involved over-the-road testing of four fatigue management technologies (FMTs) in trucking operations in Canada and the United States. Technologies bundled into a single intervention came from four fatigue management domains: one providing objective information on driver sleep need, one providing objective information on driver drowsiness, one providing objective information on lane tracking performance, and one reducing the work involved in controlling vehicle stability while driving. The objective was to determine driver reactions to such technologies and whether FMT feedback would improve alertness, especially during night driving, or increase sleep time on workdays or nonworkdays. A within-subjects crossover design was used to compare the effects of FMT feedback to no feedback. Each driver underwent the conditions in the same order: 2 weeks of no feedback (control) followed by 2 weeks of FMT feedback (intervention). Data from the devices and other driving performance variables were recorded every second of truck operation for 28 days for each driver, with a resulting 8.7 million data records among the 38 drivers. Support was found for FMT effects. During night driving, FMT feedback significantly reduced driver drowsiness (p = 0.004) and lane tracking variability (p = 0.007). However, there was evidence from probed psychomotor vigilance task testing that these improvements may have had cost because of the effort (in attention and compensatory behaviors) required to respond to information from the devices. In general, participants agreed that commercial drivers would benefit from FMT and were more positive about those involving vehicle monitoring than those involving driver monitoring.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".