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Record W2905242569 · doi:10.1109/antem.2018.8572867

An RFID Sensor for Early Expiry Detection of Packaged Foods

2018· article· en· W2905242569 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
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAntenna (radio)ConductivityComputer scienceFood industrySIGNAL (programming language)Food productsPolymerElectrical conductorElectrical engineeringAutomotive engineeringTelecommunicationsOptoelectronicsProcess engineeringMaterials scienceEngineeringFood sciencePhysicsChemistryComposite material

Abstract

fetched live from OpenAlex

An RFID sensor with the application in the food industry is proposed and investigated. The sensor can be used to evaluate the expiry date of foods. It consists of an RFID tag (the IC and the antenna) and a conductive polymer. The operational mechanism of the proposed sensor is based on the conductivity change of a polymer when it is exposed to the spoiled food. The results show increment of the read range due to decreases of the polymer conductivity at the sensing region influenced by exposure to the rotten food. Consequently, the reader installed at a certain distance can read/detect the RFID signal of a close-to-spoiled food.

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 categoriesnone
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 score0.255
Threshold uncertainty score0.276

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)0.0000.000

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 head0.010
GPT teacher head0.249
Teacher spread0.238 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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