FSCBlock: Designing financial smart contracts on permissioned and public blockchains
Bibliographic record
Abstract
Blockchain technology, though still at its infancy, is disrupting current business models by making intermediary services obsolete and the term has become a buzzword worldwide. A lot of research is going on to harness its full potential in many fronts from small to large corporate businesses. Currently, the Collateral Contract Services (CCS) are manually processed in financial institutions. The main objective of this research is to choose the most appropriate financial instrument, specifically derivatives, for CCS and allow its automated trading using Blockchain technology, which we have achieved on two prominent Blockchain platforms. In the first part of the thesis, formulating one of the derivatives, options, as a smart contract has been studied, wherein I have successfully generated and analyzed an options smart contract for Ethereum (public). Then, in the second part of the thesis, designing a Chaincode for CCS is researched using Hyperledger Fabric (permissioned), maintaining a transparent distributed ledger for the CCS between financial institutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".