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Record W2093938626 · doi:10.4018/ijaeis.2014040104

Agricultural and Environmental Applications of RFID Technology

2014· article· en· W2093938626 on OpenAlexaff
Rahma Zayou, Mohamed Amine Besbe, Habib Hamam

Bibliographic record

VenueInternational Journal of Agricultural and Environmental Information Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsRadio-frequency identificationAntenna (radio)WirelessProduct (mathematics)TelecommunicationsProcess (computing)Information and Communications TechnologyFocus (optics)Electrical engineeringEngineeringCode (set theory)Identification (biology)Field (mathematics)Computer scienceComputer securityWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

RFID (Radio Frequency IDentification) technology bridges two technologies in the area of Information and Communication Technologies (ICT), namely Product Code (PC) technology and Wireless technology. This broad-based rapidly expanding technology impacts business, environment and society. The operating principle of an RFID system is as follows. The reader starts a communication process by radiating an electromagnetic wave. This wave will be intercepted by the antenna of the RFID tag, placed on the item to be identified. An induced current will be created at the tag and will activate the integrated circuit, enabling it to send back a wave to the reader. The reader redirects information to the host where it will be processed. RFID is used for wide range of applications in almost every field (Health, education, industry, security, management …). In this review paper, the authors will focus on agricultural and environmental applications.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.002
GPT teacher head0.162
Teacher spread0.160 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2014
Admission routes1
Has abstractyes

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Same venueInternational Journal of Agricultural and Environmental Information SystemsSame topicRFID technology advancementsFrench-language works237,207