Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The principal objective of the present work was to examine the effects of mind state (mind-wandering vs. on-task) on driving performance in a high-fidelity driving simulator. BACKGROUND: Mind-wandering is thought to interfere with goal-directed thought. It is likely, then, that when driving, mind-wandering might lead to impairments in critical aspects of driving performance. In two experiments, we assess the extent to which mind-wandering interferes with responsiveness to sudden events, mean velocity, and headway distance. METHOD: Using a car-following procedure in a high-fidelity driving simulator, participants were probed at random times to indicate whether they were on-task at that moment or mind-wandering. The dependent measures were analyzed based on the participant's response to the probe. RESULTS: Compared to when on-task, when mind-wandering participants showed longer response times to sudden events, drove at a higher velocity, and maintained a shorter headway distance. CONCLUSION: Collectively, these findings indicate that mind-wandering affects a broad range of driving responses and may therefore lead to higher crash risk. APPLICATION: The results suggest that situations that are likely associated with mind-wandering (e.g., route familiarity) can impair driving performance.
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.
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it