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Record W2407832777 · doi:10.1145/2858036.2858485

RetroFab

2016· article· en· W2407832777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsRetrofittingComputer scienceInterface (matter)EnclosureToolboxUser interfaceRigidity (electromagnetism)ActuatorHuman–computer interactionEngineeringTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

We present RetroFab, an end-to-end design and fabrication environment that allows non-experts to retrofit physical interfaces. Our approach allows for changing the layout and behavior of physical interfaces. Unlike customizing software interfaces, physical interfaces are often challenging to adapt because of their rigidity. With RetroFab, a new physical interface is designed that serves as a proxy interface for the legacy controls that are now operated by actuators. RetroFab makes this concept of retrofitting devices available to non-experts by automatically generating an enclosure structure from an annotated 3D scan. This enclosure structure holds together actuators, sensors as well as components for the redesigned interface. To allow retrofitting a wide variety of legacy devices, the RetroFab design tool comes with a toolkit of 12 components. We demonstrate the versatility and novel opportunities of our approach by retrofitting five domestic objects and exploring their use cases. Preliminary user feedback reports on the experience of retrofitting devices with RetroFab.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.008
GPT teacher head0.223
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations109
Published2016
Admission routes1
Has abstractyes

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