Geared Decisions: Experimenting with Decision Support Visualizations
Bibliographic record
Abstract
Human-Machine Interfaces (HMI) are used where people and machines or systems (often within an industrial context) interact during a given task. The goal of this interaction is for the user to operate and control the machine in an effective manner, while receiving helpful and timely feedback. In some contexts, an HMI may also function as a decision support system (DSS), or include DSS components, aiding users in making effective decisions about some aspect of the industrial operation. In this paper, we discuss the iterative design of an HMI–DSS based on a mathematical model for an ice- cream manufacturing operation. We chose ice-cream manufacturing as a working example because it is a multi-modal system, containing sufficient process complexity to be generalizable to many other kinds of industrial operations. One of our design goals is to provide users with a display that includes both quantitative and qualitative information types related to the situation requiring a decision. In addition, we aim to provide users with an exploratory environment that enables them to experiment with decision alternatives, their past and potential consequences, prior to actually carrying out the decision.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".