Designing Effective Human-Automation-Plant Interfaces: A Control-Theoretic Perspective
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
In this article, we propose the application of a control-theoretic framework to human-automation interaction. The framework consists of a set of conceptual distinctions that should be respected in automation research and design. We demonstrate how existing automation interface designs in some nuclear plants fail to recognize these distinctions. We further show the value of the approach by applying it to modes of automation. The design guidelines that have been proposed in the automation literature are evaluated from the perspective of the framework. This comparison shows that the framework reveals insights that are frequently overlooked in this literature. A new set of design guidelines is introduced that builds upon the contributions of previous research and draws complementary insights from the control-theoretic framework. The result is a coherent and systematic approach to the design of human-automation-plant interfaces that will yield more concrete design criteria and a broader set of design tools. Applications of this research include improving the effectiveness of human-automation interaction design and the relevance of human-automation interaction research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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