Blockchain-Powered Smart System Platform Development: Use Case – Water Meter Readings
Bibliographic record
Abstract
Abstract Today, blockchain technology represents a significant advancement in the field of data management, providing promising opportunities to transform various sectors. Indeed, it offers innovative solutions and builds trust within multi-stakeholder business networks. This article looks at multiple concrete applications of blockchain, highlighting its potential for optimizing water meter reading processes. Moreover, there’s a growing trend toward incorporating Artificial Intelligence (AI) for more accurate and efficient water meter readings. This integration enhances the precision and efficiency of water meter readings, providing valuable insights into water consumption. More specifically, it explores how this technology can be integrated into the development of a dedicated Android application. Thanks to this application, users can now easily and quickly take water meter readings from their smartphone. The data is then secured on the Ethereum blockchain, using smart contracts to guarantee the integrity and transparency of the information recorded. This revolutionary approach opens new perspectives for efficient and secure data management in the field of water resources management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".