The Purview of Blockchain Appositeness in Computing Paradigms: A Survey
Bibliographic record
Abstract
Blockchain technology is getting more and more pertinent to solve most of the digital problems that we face today. Blockchain is notable for its prominent features like immutability, decentralization, consensus, privacy, and security. However, blockchain is still suffering from different barriers like quantum attacks, scalability problems, integration problems, incompetence to face bigdata, storage problems, and so on. The main aim of this study was to find out the scope, various problems raised, and the applicability of blockchain technology when integrated with different computing paradigms like cloud computing, edge computing, fog computing, osmotic computing, big data computing, and quantum computing. To conduct this study, we have surveyed different research articles in the combination of blockchain technology and computing paradigms. Based on this survey, we have mentioned the contemporary research works, challenges, and a list of possible research opportunities and solutions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".