MétaCan
Menu
Back to cohort

Radio frequency identification (RFID) based corrosion monitoring sensors Part 2 – Application and testing of coating materials

2014· article· en· W2119186668 on OpenAlexaff
Youliang He, S. McLaughlin, J. S. H. Lo, Chao Shi, Jared Lenos, A. Vincelli

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsImperial Oil (Canada)Defence Research and Development CanadaCanadian Sport Centre PacificUniversity of WaterlooNatural Resources Canada
Fundersnot available
KeywordsCorrosionCoatingElectromagnetic interferenceRadio-frequency identificationMaterials scienceElectromagnetic shieldingEMIShieldWirelessRadio frequencyInterference (communication)AcousticsComputer scienceTelecommunicationsComposite materialPhysics

Abstract

fetched live from OpenAlex

Cost-effective radio frequency identification (RFID) transponders (tags) were investigated for wireless corrosion monitoring by applying a metal filled conductive composite coating onto the surface of the plastic tags. The coating acted as an electromagnetic interference (EMI) shield by attenuating or completely blocking the radio frequency signals transmitted between the transceiver (reader) and the tag. When the coated RFID tag was exposed to a corrosive environment through accelerated corrosion tests or marine atmospheric tests, the degradation of the coating decreased the EMI shielding effectiveness and resulted in a strengthened communication between the reader and the tag. By establishing a correlation between the corrosion rate of the metal object to be monitored and the sensor responding performance, it was possible to monitor cumulative corrosion damage via wireless sensor reading.

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 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)0.002
metaresearch head score (Gemma)0.002
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 score0.094
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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 teacher head, 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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion ControlSame topicSmart Materials for ConstructionFrench-language works237,207