Influence of Information Layout on Diagnosis Performance
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.
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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 | 0.004 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".