Bibliographic record
Abstract
Blockchain is an emerging technology that has encountered a number of challenges, including forks and low transaction processing rates compared to other payment systems. While sharding was designed to resolve the problem of low processing rates, it also increases the scalability of the network. However, its effect on the probability of forking is still unclear. Our primary goal in this study is to determine the impact of adding new shards to a blockchain on the probability of forks occurring. In order to achieve this goal, we first developed a novel simulator which enables us to simulate sharded networks. Then, we examined the effect of sharding on fork occurrence. Two EIP-1559 enabled networks containing 60 and 120 nodes have been studied in several experiments. As a result of our study, we have found that adding one shard on average results in a 60% reduction in the number of orphan blocks. In addition, we have proposed a fork probability model which results in 23% and 15% reductions for networks with 60 and 120 nodes, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".