Demonstration of Mixels: Fabricating Interfaces using Programmable Magnetic Pixels
Bibliographic record
Abstract
demonstration Share on Demonstration of Mixels: Fabricating Interfaces using Programmable Magnetic Pixels Authors: Martin Nisser MIT CSAIL, UnitedStates MIT CSAIL, UnitedStates 0000-0003-3294-9555View Profile , Yashaswini Makaram MIT CSAIL, UnitedStates MIT CSAIL, UnitedStatesView Profile , Lucian Covarrubias MIT CSAIL, UnitedStates MIT CSAIL, UnitedStatesView Profile , Amadou Yaye Bah MIT CSAIL, UnitedStates MIT CSAIL, UnitedStatesView Profile , Faraz Faruqi MIT CSAIL, UnitedStates MIT CSAIL, UnitedStatesView Profile , Ryo Suzuki University ofCalgary, Canada University ofCalgary, CanadaView Profile , Stefanie Mueller MIT CSAIL, UnitedStates MIT CSAIL, UnitedStatesView Profile Authors Info & Claims UIST '22 Adjunct: Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and TechnologyOctober 2022Article No.: 114Pages 1–3https://doi.org/10.1145/3526114.3558654Published:20 December 2022Publication History 0citation42DownloadsMetricsTotal Citations0Total Downloads42Last 12 Months42Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".