A Blockchain-based System for Aid Delivery: Concept Development, Data Modeling, and Validation
Bibliographic record
Abstract
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 (eg, 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 studeis validate the applicability of this proposed system showing high throughput and satisfactory performance are attainable with integration of blockchani in large-scale aid delivery. Keywords: Aid Delivery, Blockchain, Data Flow Management, Data Model for Delivery, Disaster Management, Drone Technology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".