Adaptive In-Vehicle Information Systems and Their Usability Evaluation
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
<div class="htmlview paragraph">In-Vehicle Information Systems (IVIS) provide vehicle travelers with a range of useful information, including road condition, weather broadcasting, GPS maps, and city navigation. It is widely acknowledged that a single IVIS design does not fit everyone as users can have different interface and content preferences. T hese preferences are often related to age, gender, experience, and other demographic, social, and psychological characteristics. IVIS need to be capable of adapting to the context. This paper reviews adaptation techniques found in user-adaptive systems and develops a mapping between adaptation techniques and the characteristics of the system being adapted. This mapping is then used to show that adaptation techniques for user-adaptive systems can be applied to the design of IVIS. As IVIS become popular and their functionalities become more diverse, driver distraction will increase due to increased cognitive load. Since inappropriate adaptation can lead to user confusion and distraction, the usability of adaptive IVIS need to be carefully evaluated.</div>
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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