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Record W2997759229 · doi:10.3390/electronics9010106

Electromagnetic Soil Characterization for Undergrounded RFID System Implementation

2020· article· en· W2997759229 on OpenAlexaff
Ayman Elboushi, Ahmad A. Telba, Abdel-Razik Sebak, Khalid Jamil

Bibliographic record

VenueElectronics · 2020
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsConcordia University
FundersNational Plan for Science, Technology and InnovationKing Abdulaziz City for Science and Technology
KeywordsAntenna (radio)ChipEngineeringElectronic engineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

This paper can be divided into two main parts. In the first part, an extensive experimental study for electromagnetic characteristic assessment of different soil samples is presented. In the second part of the paper, a practical verification of the obtained link budget model is performed using a buried metal-backed RFID tag antenna under a 40 cm sand layer. This antenna is designed to operate at 915 MHz with MONZA3 chip. Using Impinj Speedway system the tag antenna could be detected, and its information could be read from different distances of up to more than 2.5 m.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.007
GPT teacher head0.224
Teacher spread0.216 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes1
Has abstractyes

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