Blockchain Technology in the Process of Financing the Construction and Purchase of Commercial Vessels
Bibliographic record
Abstract
The share of European shipbuilding in the world market, with the constant exception of the cruise ship niche, has been in continuous decline for decades, while at the same time, state-supported Asian competitors are recording accelerated growth. With the already long-standing ban on subsidizing the shipbuilding industry by EU member states, its shipyards can maintain their market position primarily by continuously improving business processes, including adopting Industry 4.0 doctrines. In order to contribute to the European shipyards’ competitiveness growth, the authors of this paper use the case study methodology to investigate the applicability of blockchain technology in the process of financing the construction and purchase of ships according to a bareboat charter model, which is recognized as risk-balanced for all parties involved in the process. The empirically analyzed implementation of the blockchain application of smart contracts, using the example of three ships built and purchased according to the proposed model, theoretically results in an almost one-year shortening of the financing process, with a significant reduction in the costs of legal activities. The originality of this study is also emphasized by the correlation of smart contracts and the process of early ship outfitting in the sense of the possible improvement in its level, thus achieving savings in working hours and energy and ultimately shortening the process of realizing the shipbuilding project.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".