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Record W2618709089 · doi:10.1109/jsen.2017.2707525

On-Chip Electronic Nose For Wine Tasting: A Digital Microfluidic Approach

2017· article· en· W2618709089 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

VenueIEEE Sensors Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsUniversity of Prince Edward IslandUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsElectronic noseWineWine tastingMicrofluidicsMaterials scienceComputer scienceBiological systemNanotechnologyChemistryFood science

Abstract

fetched live from OpenAlex

This paper presents the integration of a metal oxide gas sensor into an open digital microfluidic (DMF) system for rapid identification of different types of wines. The effect of water-cross sensitivity of a gas sensor was eliminated using a hydrophobic porous micro-channel, which provides selective detection of the aromas of a wine droplet manipulated on a DMF platform. Seven different kinds of wines are tested and successfully recognized. The temporal responses of the sensor to a droplet of each of these wines are reported. Two features are extracted from the transient response and mapped into a 2-D plot to show the segregation of different wines.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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