Experimentation for Decentralized Resource-based Multi-pool Mining in Ethereum Blockchain
Bibliographic record
Abstract
The potential of having multiple distributed applications across multiple domains such as healthcare, finance, supply chain management and many more have made blockchain very popular among both academia and industries. Blockchain provides the much-needed mechanism for decentralization of systems, where the need of trusted central authority is eliminated. Proof of Work (PoW) is heavily adopted in both Bitcoin and Ethereum based blockchain, where many miners (or mining pools) compete to mine each block generated by solving a cryptographic puzzle that uses all of the previous information of the blockchain, before tying the block to the blockchain with the nonce. Recently there is a shift in having two or more mining pools for the PoW consensus in Ethereum-based blockchain. Majority of the studies conducted for multiple mining pool techniques in the literature are verified with simulation experiments. Therefore, in this paper, we implement a testbed for Ethereum blockchain with multiple nodes that simulate two mining pools using PoW consensus in a centralized and decentralized fashion. Two miner-nodes were deployed with different computational power (in term of CPU threads) and transactions executed. We evaluate the contribution of each miner node in the blockchain system and the assignment of transactions with respect to the computational resources available.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".