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RFID and Wireless Personal Area Networks for Supply Chain Management

2011· book-chapter· en· W4252861010 on OpenAlexaff
David Wright

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRadio-frequency identificationWirelessSupply chainPersonal area networkSupply chain managementWireless networkPersonal accountComputer scienceTelecommunicationsBusinessMarketingComputer security

Abstract

fetched live from OpenAlex

Efficient supply chain management relies on knowing where products in the supply chain are located. The ability to track items from manufacturing plant to warehouse to distribution center to wholesaler to retailer is currently provided by RFID, radio frequency identification (Weinstein, 2005). Case examples of commercial applications of RFID in supply chain management are evaluated by Jones et al. (2004). A recent development, low power wireless personal area networking, WPAN, can offer advantages over RFID in certain circumstances. It is the purpose of this article to evaluate RFID and wireless personal area networks with respect to each other and to identify the features that give one an advantage over the other. We first describe the two technologies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.201
Teacher spread0.190 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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