Three Navigation Systems With Three Tasks: Using the Lane-Change Test (LCT) to Assess Distraction Demand
Bibliographic record
Abstract
The Lane Change Test (ISO, 2008; Mattes, 2003) was used to assess distraction demand when drivers completed three typical navigation tasks (an easy navigation task, a point of interest task and a difficult navigation task) using three different navigation systems. In order for the LCT to be a useful procedure, it must distinguish good from poor navigation systems and acceptable from unacceptable tasks performed using those systems. The results provide some general support for the LCT as a sensitive measure of distraction. Some aspects of the results, however, called into question the adequacy of the LCT as a sufficient measure of distraction. In particular, the LCT was found to be insensitive to task demands arising from excessive task duration. Since risk exposure is a function of secondary task duration (as well as other factors such as intensity, frequency and timing), it is recommended that a measure of task duration be incorporated in the LCT procedure. When the MDEV was modified to incorporate task duration, the resulting measure (mean deviation per average task) reflected more adequately the interaction demands of the various navigation tasks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".