Bibliographic record
Abstract
number of microsleeps during the driving task, steering deviation, braking reaction time and crashes all negatively correlated with the first MWT sleep latency. Using a receiver-operator characteristic curve, the authors found that first MWT sleep latency in the partial sleep deprivation plus alcohol condition significantly discriminated subjects who had a crash from those who did not. They conclude that sleep latency on the MWT is a reasonable predictor of driving simulator performance at least in sleepy, alcohol-impaired, normal subjects. While it is reassuring that impairment due to combined effects of sleepiness and alcohol can be detected by their driving simulator and that MWT tracks this impairment reasonably well, the applicability of these results is far from clear. Only 1 (albeit fairly important) driving simulator measure (braking reaction time) was significantly correlated with MWT whereas many more were brought out by the additive (and/or synergistic) effect of alcohol. It is already established that mild sleepiness is exaggerated by low dose alcohol 15 and while the results may then apply to cases where both condition apply, many motor vehicle collisions occur without any alcohol involved. Moreover, peak accident frequencies usually occur much later than 0100 hours, the time tested in this paradigm. 16
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".