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Record W2941502270 · doi:10.3233/978-1-61499-951-5-513

Applying a Pneumatic Interface to Intervene with Rapid Eating Behaviour

2019· article· en· W2941502270 on OpenAlexaff
Zuoyi Zhang, Junhyeok Kim, Yumiko Sakamoto, Teng Han, Pourang Irani

Bibliographic record

VenueStudies in health technology and informatics · 2019
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFidelityInterface (matter)Computer scienceHuman–computer interactionEating behaviorObesityPsychologyMedicineOperating systemTelecommunications

Abstract

fetched live from OpenAlex

Higher eating rates are positively correlate with obesity. In this paper, we propose the design of a new eating utensil that can reduce eating rate by interfering with eater's ability to eat quickly. This utensil can change its rigidity and shape by deflating itself to interfere with eating. In this study, a low fidelity proof-of-concept prototype device has been designed to provide physical resistance in order to help people reduce their eating rate. The proposed prototype could be used to demonstrate the feasibility of applying a pneumatically actuated shape-changing interface to embed physical resistance into an eating utensil.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.345
Teacher spread0.317 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2019
Admission routes1
Has abstractyes

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