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Record W2489911370

RFID-enabled materials management in the industrial construction supply chain

2011· article· en· W2489911370 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

VenueInternational Conference on Communications · 2011
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRadio-frequency identificationSupply chainSupply chain managementProduct (mathematics)Identification (biology)Quality (philosophy)Computer scienceBusinessManufacturing engineeringIndustrial organizationEnvironmental economicsSystems engineeringRisk analysis (engineering)TelecommunicationsEngineeringMarketingComputer security
DOInot available

Abstract

fetched live from OpenAlex

The construction industry, which accounts in average for 6.5 percent of Gross Domestic Product in OECD countries, represents a vital segment of the economy. Among the array of innovative Information and Communication Technologies that could be deployed in this sector, the radio frequency identification (RFID) technology stands out as a radical innovation that could enhance the efficiency of material flows between construction supply chain members, and consequently help to meet project deadlines. This paper, based on an exploratory field research, analyzes the potential of RFID for the management of materials across four layers of one construction supply chain. By allowing the identification and localization of materials in real time, RFID can lead to substantial cost reductions. Furthermore, an RFID-enabled materials management system will ensure more accurate inventories, more efficient quality controls, and, overall, a smoother optimization of day-to-day materials management.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.675

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.124
GPT teacher head0.299
Teacher spread0.175 · 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