Emerging LNG-fueled ships in the Chinese shipping industry: a hybrid analysis on its prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it