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Record W2913115916 · doi:10.1002/itl2.93

Research challenges and opportunities in blockchain and cryptocurrencies

2019· article· en· W2913115916 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

VenueInternet Technology Letters · 2019
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCryptocurrencyBlockchainDistributed ledgerIntermediaryLedgerIncentiveProof-of-work systemDigital currencyProtocol (science)Computer scienceComputer securityState (computer science)Peer-to-peerCommerceBusinessData scienceWorld Wide WebFinanceEconomics

Abstract

fetched live from OpenAlex

The blockchain is the underlying technology of the Bitcoin cryptocurrency, and it has created much excitement in the technology and research communities. A blockchain is a distributed ledger collectively maintained by a peer‐to‐peer network of participants who in Bitcoin are known as miners. This key innovation enables cryptocurrencies such as Bitcoin to operate in a decentralized manner with no intermediaries such as financial institutions. But the blockchain can be used to record things other than cryptocurrency transactions. While many of the concepts of Bitcoin build on what have been around since the 1980s and 1990s, the designer(s) of it have made important assumptions that make it work along with the use of an incentive protocol, leading to a major breakthrough from traditional academic thinking. In this paper, we present the state‐of‐the‐art of blockchain and cryptocurrencies along with research challenges and opportunities that would be of interest to researchers getting into this exciting field.

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)0.000
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: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.052
GPT teacher head0.283
Teacher spread0.232 · 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