Empirical Evaluation of an Industrial Application of Ecological Interface Design
Bibliographic record
Abstract
Abnormal events in production plants cost the petrochemical industry billions of dollars annually. In part, these events are difficult to deal with because current interfaces do not adequately inform operators about the state of the process. Ecological human-machine interfaces aim to provide information about higher-level process functions. Several laboratory simulator studies have shown that, in comparison with contemporary process interfaces, ecological interfaces can lead to faster fault detection, better root-cause diagnosis, and more effective control responses. However, an empirical evaluation of these findings for professional operators in more realistic plant settings has been absent from the literature. In this study, two ecological interfaces were created for a representative petrochemical refining process. One was a traditional ecological interface based on a system-based analysis and the other was an ecological interface augmented with additional task-based information. Professional operators used the novel interfaces in an industrial simulator to monitor for, diagnose, and respond to several types of process events. In comparison to operators using the current process interface, participants in both ecological interface conditions showed better control performance, while the participants using the augmented ecological interface provided more accurate fault diagnoses than either of the other two groups. The results shed light on practical implications for the use of ecological interfaces in the process industries.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".