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Record W2183799819 · doi:10.11575/prism/30751

Shared Phidgets: A Toolkit for Rapidly Prototyping Distributed Physical User Interfaces

2006· article· en· W2183799819 on OpenAlexfundno aff
Nicolai Marquardt, Saul Greenberg

Bibliographic record

VenuePRISM (University of Calgary) · 2006
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsComputer scienceProgrammerRapid prototypingSoftwareOperating systemInterface (matter)User interfaceEmbedded systemDistributed computingHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Many physical user interfaces are best viewed as an interacting collection of remotely-located distributed hardware and software components. The problem is that current physical user interface toolkits do not normally offer distributed systems capabilities, leaving developers with extra burdens such as device discovery and management, lowlevel hardware access, and networking. Our solution is Shared Phidgets, a toolkit for rapidly prototyping distributed physical interfaces. This toolkit offers programmers several ways to easily access remotely located hardware components, including a powerful distributed model-viewcontroller object model. Network communication and lowlevel access to the device hardware are transparently handled, regardless of device location. The programmer can also create new abstract devices by transforming and aggregating low level hardware device capabilities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0050.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.010

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

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