Making despite Material Constraints with Augmented Reality-Mediated Prototyping
Bibliographic record
Abstract
We present a discussion on designing an Augmented Reality (AR)-based prototyping approach to help makers continue building low-fidelity physical computing projects despite material constraints and demonstrate an example, Polymorphic Cube (PMC). Lack of immediate or easy access to electronics is a roadblock to building physical computing projects. We present AR-mediated prototyping as an approach where mobile AR can be used to simulate missing I/O components in-situ during electronics prototyping. Using our suggested approach makers can build a circuit with available real-world materials, substitute the missing components using any augmented physical proxy, and continue implementation tinkering and interaction with the hybrid circuit. Evaluation of PMC demonstrated that users can leverage computing to overcome the lack of electronic components and build low-fidelity prototypes to support design thinking. Our study revealed the benefits and limitations of our current prototype system and encourages future explorations into an AR-mediated prototyping approach to making.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".