Bibliographic record
Abstract
The world experienced unprecedented growth in international trade in the past few decades. In this resulting environment of global competition, several new companies have spawned and are spawning. Sellers are often under immense pressure by the market players to accept open-account trade terms, shipping goods before receiving payment, leaving them exposed to increased risk. This creates working capital challenges for firms, especially small and medium enterprises. To mitigate this supply chain risk, several supplier-led and buyer-led supply chain finance solutions are facilitated by banks and financial technology companies. However, because of the new Basel III regulation framework for banks and several other Supply Chain Finance (SCF) adoption barriers, like fraudulent activities, many firms are unable to reap SCF’s full benefits. This study explains various SCF instruments, the key drivers in their growth and their adoption barriers. This study then focuses on the novel blockchain technology and smart contracts by delving deep into their history, components, limitations, risks and use cases. Using the knowledge gathered in the process, a proof-of-concept blockchain and smart contract is developed using the Ethereum platform, which can facilitate normal business, purchase order finance, reverse factoring and reverse securitization. To test the smart contract, four use cases for each SCF instrument mentioned are demonstrated. A JavaScript-based unit test is done to test the smart contract’s correct deployment and onboarding of actors along with their business and financing interactions – access controls to business documents, reverting malicious transactions and correct fund transfer. As a result of various assumptions taken in the development process, the smart contract works only as a basic proof-of-concept and lacks robustness on the ground of scalability and security. As a result, a future model is laid out which will use various software and hardware oracles for autonomous operations while using a complex system of a storage contract, a permanent contract which stores all the data, and logic contract, upgradable contract which can be changed any number of times, with a proxy contract making delegated “function” calls to logic contract to reduce the gas usage due to external function calls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".