Putting It All Together: Improving Display Integration in Ecological Displays
Bibliographic record
Abstract
Computer displays are being designed for increasingly larger industrial systems. As the application domain scales up, maintaining integration across different kinds of views becomes more challenging. This paper presents the results of a study of three different approaches to integration based on the spatial and temporal proximity of related information objects. The domain used for evaluation was a simulation of an industry-scale conventional power plant. All three displays were ecological displays developed using an abstraction hierarchy analysis. Views were integrated in a high-space/low-time, low-space/high-time, and high-space/high-time integration of means-end related objects. During a fault detection and diagnosis task, it was found that a low level of integration, high-space/ low-time, provided the fastest fault detection time. However, the most integrated condition, high-space/high-time, resulted in the fastest and most accurate fault diagnosis performance. Actual or potential applications of this research include computer displays for large-scale systems such as network management or process control, for which problem solving is critical and integration must be maintained.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".