A blockchain-based storage intelligent
Bibliographic record
Abstract
A blockchain is a distributed and decentralized database that allows users to securely store and exchange data without requiring the intervention of a third party. Information, called transactions, is collected in blocks, which are then linked together via cryptographic processes in an irreversible way. The registry has served as a historical record of all actions taken by participants in the network since its launch. An increase in the number of transactions, leading to a considerable increase in the size of the primary blockchains, making them more difficult to maintain, perhaps discouraging certain nodes from storing the entire blockchain and therefore weakening decentralization. In this study, we introduce the low-storage node, a new type of node that stores chunks of blocks rather than whole blocks and is encoded with an erasure code. A low-storage node recovers the initial block by downloading and decoding a enough encoded fragments from other nodes in the network. This strategy has the advantage of allowing certain nodes to keep a reduced version of the blockchain while contributing to its decentralization. This simplifies the scaling of blockchains, which is one of the main flaws of the technology. BlockStock is a complete system that allows nodes to rent out its additional storage space to others. The main innovation is the use of blockchain-based smart contracts that enable frequent, automated and secure payments based on proofs of recovery provided by storage servers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".