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Record W2981942933 · doi:10.33832/ijast.2019.128.02

Blockchain Technology: The New Internet Logistic Brain

2019· article· en· W2981942933 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 Journal of Advanced Science and Technology · 2019
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsLakehead University
Fundersnot available
KeywordsBlockchainComputer scienceThe InternetComputer securityWorld Wide Web

Abstract

fetched live from OpenAlex

Everyone in any collaborative business or industry has been discussing how to tab on the blockchain technology. While we are still trying to get a grasp of concepts such as consensus algorithms and distributed ledgers, top-notch industry developers are expanding and strengthening blockchain technology with highly complex, promising and intriguing innovations. Blockchain technology becomes the new logistic brain of the Internet as it involves the creation of data "blocks," detailing actions for a given business transaction or actions, and such information is finalized and locked into a chain. The chain is only added to with each transaction, so the origin of transaction details, such as financial records, product details, and location, can be traced. Thus, all subsequent business transactions can be verified and tracked, enhancing transparency and visibility into the transaction. In addition, blockchains may be public or private, granting or denying access to the chain details based on authorization, so private information can be protected, while allowing addresses to authorized parties. The future of using the Internet as the supply chain is limitless with the power of blockchain 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.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0050.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.269
Teacher spread0.261 · 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