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Record W4366548401 · doi:10.4018/jdm.321757

A Blockchain-Based System for Aid Delivery

2023· article· en· W4366548401 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Database Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsToronto Metropolitan University
FundersConnaught Fund
KeywordsBlockchainDroneObstacleComputer securityComputer scienceProcess (computing)Scale (ratio)Humanitarian aidProcess managementRisk analysis (engineering)BusinessEngineering managementEngineering

Abstract

fetched live from OpenAlex

Climate-related catastrophes leave people in dire need of aid. A major obstacle in providing help to people is the lack of trust in the aid process. Charity organizations want to ensure that funds and materials reach the intended destinations. Blockchain technology injects trust into business transactions through impeccable record keeping and can alleviate the trust problems in aid delivery. Another major problem in disaster recovery is broken infrastructure (e.g., broken bridges and unavailable roads). Unmanned aerial vehicles (UAV), generally referred to as drones, can address this access problem. In this paper, the authors design a system that uses drone technology for delivery of aid and blockchain technology for the assurance of such delivery. This system records and shares data on the interaction of various participants involved in a disaster aid delivery scenario. The simulation studies validate the applicability of this proposed system showing high throughput and satisfactory performance are attainable with integration of blockchain in large-scale aid delivery.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.018
GPT teacher head0.250
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