Analysis on Blockchain Effectiveness Towards Protecting Renewable-Based Smart Power Grids
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.
Bibliographic record
Abstract
Non-renewable energies have been increasingly destructive to the environment, and as a result, society has been seeking to replace these energies at a growing rate. Converting current non-renewable-based power grids to environmentally friendly smart energy grids has been identified as one of the most powerful ways to remove the reliance on non-renewable energies. However, failing to keep these new power grids efficient and secure will cause this concept to fail to become a reality. With a new energy management system, a new security system is also required. Some developing technologies such as blockchain and artificial intelligence have been identified as candidates for strong and efficient security protocols for smart grids. Analysis shows that blockchain technology can provide incredible defense against malicious tampering and data protection. Artificial intelligence can be trained to identify attacks before they become destructive. Along with these new technologies, concepts such as the firewall should be included due to their general effectiveness and efficiency. By utilizing both old and new security protocols, a safer, more efficient, and more reliable energy grid for the future can be created.
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 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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it