Estimating Workload Demands of Turning Left at Intersections of Varying Complexity
Bibliographic record
Abstract
The challenge posed by left-turns has been well-documented in literature. Left-turns are thought to be complex roadway sites resulting in a significant proportion of motor-vehicle collisions. The purpose of the present study was to determine whether subjective and objective workload is affected by left-turns of varying complexity (i.e., information processing and maneuvering) in a sample of young inexperienced drivers. A secondary goal was to determine the effect of administering a secondary task on subjective workload. To this end, 60 inexperienced drivers completed four simulated driving scenarios of varying visual and maneuvering complexity. Half of participants completed an objective measure of workload (i.e., a secondary task) while all participants completed a subjective measure of workload upon completion of each scenario. The results demonstrated the effect of complexity on subjective and objective workload. Specifically, information processing complexity was found to significantly affect both subjective and objective measures of participants’ workload while the influence of maneuvering complexity was detected through subjective load only.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".