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Record W4405778365 · doi:10.31039/ljss.2024.8.198

BTCEN Project: Application of Blockchain Technology in Supply Chain

2024· article· en· W4405778365 on OpenAlexaff
Cihan Bulut

Bibliographic record

VenueLondon Journal of Social Sciences · 2024
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsBlockchainTraceabilitySupply chainSupply chain managementBusinessProcess managementTransparency (behavior)IntermediaryComputer scienceMarketingComputer securitySoftware engineering

Abstract

fetched live from OpenAlex

The "BTCEN Project: Application of Blockchain Technology in Supply Chain'' article examines the potential impacts of blockchain technology on supply chain management. This paper highlights how products can increase transparency, reliability, and traceability in supply chains by recording their journey from source to the end user. BTCEN aims to bring a modern perspective to the supply chain by integrating e-commerce, tokenization, CRM, and ERP modules. The project accelerates business processes and reduces costs by eliminating intermediaries, thus providing significant benefits to businesses and consumers. The article shows how blockchain technology can be a valuable tool beyond financial transactions, especially in supply chain management. The success of the BTCEN Project represents a promising example of the potential of blockchain technology to provide innovative solutions in different sectors and lays a solid foundation for future applications of this technology.

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)0.005
metaresearch head score (Gemma)0.006
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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.011
GPT teacher head0.286
Teacher spread0.274 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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