ViSA: A Parametric Design Modeling Method to Enhance Visual Sensitivity Control and Analysis
Bibliographic record
Abstract
The ability of parametric computer-aided design systems to generate models rapidly enables designers to explore the downstream impacts of changes to key design parameters. However, the typical modeling functions provided in the parametric systems can become insufficient when such exploration is needed for increasingly complex parametric design models. Main challenges for exploration that we observed are control and analysis of changes on the design model and in particular, when they are introduced continuously. The system interfaces and the human-visual perception system alleviate these challenges. In this study, we demonstrate ViSA, a Visual Sensitivity Analysis method that aims to make the effects of change within a parametric model controllable, measurable and apparent for designers. The approach aims to improve visually analyzing the sensitivity of a design model to planned parametric changes. The method proposes customizable control and visualization features in the model that are decoupled from each other at the design level, while providing interfaces between them through parametric associations. We present findings from our case studies in addition to the results of a user study demonstrating the applicability and limitations of the proposed method.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".