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Record W2957199171 · doi:10.1109/iccbb.2018.8756455

Bitcoin Blockchain Transactions Visualization

2018· article· en· W2957199171 on OpenAlexaff
Ajay Kumar Shrestha, Julita Vassileva

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBlockchainCryptocurrencyDatabase transactionComputer scienceVisualizationComputer securityDatabaseData mining

Abstract

fetched live from OpenAlex

Blockchains were originally used to support the Bitcoin cryptocurrency and now entire software ecosystems are being supported by blockchains. Despite their widespread use, not much is known about how peers in the bitcoin blockchain network use the system. We present visualizations to help in identifying some patterns in the usage of bitcoin blockchain supported technologies. In this paper, we observe the bitcoin transaction continent-wise via a visualization of the locations from where peers in the bitcoin blockchain network were making their transactions, using WebGL technology. We analyzed regional bitcoin blockchain usage patterns by observing their clusters formation over time. We also presented a pattern of how the value of the bitcoin changes over time.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.943
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations19
Published2018
Admission routes1
Has abstractyes

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