Comprehending parametric CAD models
Bibliographic record
Abstract
In this study, we experimentally evaluated two GUI prototypes (named "split" and "integrated") for parametric CAD modeling. Participants in the study were asked to perform a number of 3D model comprehension tasks, using both interfaces. The tasks themselves were classified into three classes: parameterization, topological and geometrical tasks. We measured the task completion times, error rates, and user satisfaction for both interfaces. The experimental results showed that task completion times are significantly shorter when the "split" interface is being used, in all cases of interest: 1) tasks taken as a whole and 2) tasks viewed by task type. There was no significant difference in error rates between the two interfaces; however, error rate was significantly higher in the case of parameterization tasks (for both interfaces). User satisfaction was significantly higher for the "split" interface. The study gave us a better understanding of the human performance when perceiving and comprehending parametric CAD models, and offered insight into the usability aspects of the two studied interfaces; we also believe that the knowledge obtained could be of practical utility to implementers of parametric CAD modeling packages.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".