Influence of Information Layout on Diagnosis Performance
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
Effective diagnosis performance is necessary for the operation of safety-critical industrial systems. Diagnosis depends on the information provided, perceived, interpreted, and integrated by operators. This paper examines the influence of information layout on diagnosis performance. Three layouts were designed to meet the information requirements identified through a work domain analysis and task analysis. One interface depicted the vertical means-end relations in the abstraction hierarchy, a second depicted the horizontal relations between nodes, and a third followed a conventional mimic layout. Because vertical means-end relations present a clear mapping between functional and physical information, it was hypothesized that the vertical interface would facilitate more effective use of functional information and thereby better support diagnosis performance compared with the horizontal and mimic interfaces. No significant influence of information layout on diagnosis accuracy or completion time was found. However, the participants who used the vertical and horizontal interfaces were more confident with their diagnosis conclusions than those using the mimic interface. In addition, the participants using the vertically integrated interface spent significantly less time generating correct hypotheses than the participants using either the horizontal or mimic interfaces. These findings stress the importance of information layout for interfaces of safety-critical systems.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it