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Record W2799691881 · doi:10.5931/djim.v14i0.7869

Blockchain Tracking and Cannabis Regulation: Developing a permissioned blockchain network to track Canada's cannabis supply chain

2018· article· en· W2799691881 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2018
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsDalhousie University
FundersSamsungDeepMind
KeywordsBlockchainBusinessGovernment (linguistics)Product (mathematics)Investment (military)Quality (philosophy)CannabisSupply chainTracking (education)Computer securityIndustrial organizationMarketingComputer scienceLaw

Abstract

fetched live from OpenAlex

Achieving government’s goals for cannabis regulation requires legal cannabis to be a cheaper, more attractive consumer alternative compared to the illegal market. This goal may be undermined by the costs and disadvantages of traditional regulatory management. A Canada wide, real-time blockchain tracking system appears to be a viable technical solution architecture. A permissioned blockchain network could be tested alongside traditional tracking. This investment, if proven effective, could reduce regulatory costs for government and red tape for business, helping to achieve Governments’ objectives to:Enhance public safety by ensuring quality and monitoring product salesUndermine illegal markets to reduce crime and prevent product diversion

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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