Emerging LNG-fueled ships in the Chinese shipping industry: a hybrid analysis on its prospects
Bibliographic record
Abstract
Abstract With the prosperity of global shipping industry, a variety of shipping-induced environmental problems and increasingly rigid emission restrictions have drawn more attention to an emerging marine fuel—liquefied natural gas (LNG), a clean and efficient energy that conforms to the essence of green shipping. Although with superiority using onboard, there are only fewer than 40 fully LNG-fueled ships in service worldwide by June 2013, and the quantity of LNG-fueled ships in operation is even much lower in China. Moreover, the majority of those in China are inland ships, which mainly navigate along the Yangtze River and canals. By using the SWOT (strengths, weaknesses, opportunities, and threats) analysis in combination with the analytic hierarchy process (AHP), this paper analyzes the development prospect of LNG-fueled ships in inland waterway transportation in China, aiming to fill the gaps of inadequate understanding of the new marine energy. This paper offers some insight on the prospects of the application of LNG in the Chinese shipping industry and simultaneously provides useful information to stakeholders and policy makers for decision-making on the development of LNG-fueled ships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".