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Record W3046249758 · doi:10.1109/map.2020.3003216

Wireless Passive Sensors for Food Quality Monitoring: Improving the Safety of Food Products

2020· article· en· W3046249758 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Antennas and Propagation Magazine · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsMcGill UniversityUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRadio-frequency identificationFood spoilageUltra high frequencyWirelessFood safetyFood qualityFood wasteComputer scienceElectrical engineeringEngineeringEnvironmental scienceTelecommunicationsWaste managementComputer securityFood science

Abstract

fetched live from OpenAlex

Food waste amounts to roughly one-third of its total production every year. There is an unprecedented demand to improve long-term storage of food products while preserving quality and safety in every stage of its processing, from postharvesting to preconsumption. Different technologies, such as total viable count (TVC), metal-oxide-semiconductor sensors, fluorescence spectroscopy, dye and polymer-based colorimetric sensors as well as radio-frequency identification (RFID), are currently applied for monitoring food products. This article provides an overview of current developments in near-field and ultrahigh frequency (UHF) wireless passive sensors for monitoring of food quality indices and food spoilage indicators. Solutions based on coupled-coil resonator and UHF chipless RFID sensors with application to bacterial-count detection, volatile gas concentration, humidity, and pH monitoring are highlighted.

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 score0.023
Threshold uncertainty score0.439

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 head0.032
GPT teacher head0.245
Teacher spread0.213 · 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