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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".