Distributed Ledger Technologies and Their Applications: A Review
Bibliographic record
Abstract
With the success of Bitcoin and the introduction of different uses of Blockchain, such as smart contracts in Ethereum, many researchers and industries have turned their attention to applications that use this technology. In response to the advantages and disadvantages of Blockchain, similar technologies have emerged with alterations to the original structure. Distributed ledger technology (DLT) is a generalized distributed technology encompassing these new variants. Several studies have examined the challenges and applications of Blockchain technology. This article explores the possibilities of using different DLTs to solve traditional distributed computing problems based on their advantages and disadvantages. In this paper, we provide an overview and comparison of different DLTs, such as Hashgraph, Tangle, Blockchains, Side Chain and Holochain. The main objective of the article is to examine whether distributed ledger technologies can replace traditional computational methods in other areas instead of traditional methods. Based on the primary keywords, we conducted a systematic review of more than 200 articles. Based on the data extracted from articles related to the use of DLT, we conclude that that DLTs can complement other methods, but cannot completely replace them. Furthermore, several DLTs such as Sidechain, Holochain and Hashgraph are still in their infancy, and we foresee much research work in this area in the coming years.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".