A Survey on Off-chain Technologies
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
Blockchain is a decentralized ledger with a secure and immutable chain structure. The advanced attributes of blockchain, including decentralization, anonymity, transparency, and zero trust support, have positioned it as a transformative technology across different areas of expertise, like medicine, finance, and the Internet of Things (IoT). Nonetheless, blockchain’s progress has been constrained in various aspects, revealing inefficiency, privacy, high transaction fees, and challenges with on-chain storage. To address these limitations, off-chain technology has emerged as a solution by moving computation and storage overhead away from the blockchain. However, a comprehensive survey on off-chain schemes is lacking in the current literature. In this article, we conduct a thorough survey on off-chain technologies. We first introduce the fundamental concepts and characteristics of both blockchain and off-chain technologies. Furthermore, we establish a thorough taxonomy of off-chain technologies based on distinct application scenarios. We put forth a series of evaluation criteria, based on which we seriously review and analyze the existing off-chain schemes to assess their strengths and limitations. Conclusively, we outline a list of open issues and propose promising future research directions based on our thorough review and analysis on off-chain technologies.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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 it