DEVELOPMENT OF NATURAL GAS OCEAN TRANSPORTATION CHAIN BY MEANS OF NATURAL GAS HYDRATE (NGH)
Bibliographic record
Abstract
While alternative natural gas transportation technologies against currently available pipeline or liquefied natural gas (LNG) are expected to develop to be suitable for small and medium or remote gas fields, Mitsui Engineering & Shipbuilding Co., Ltd. (MES) has been studying natural gas hydrate (NGH) transportation chain and advocated at ICGH2005 the NGH chain was economical compared with conventional LNG system under some conditions. Meanwhile, MES has been carrying out research and development on the relevant technology development including construction of 600 kg/day class NGH production and pelletizing plants and a re-gasification facility and the process technology resulted from this R&D leads to the forthcoming demonstration plant of 5 ton/day production (under construction) to be dedicated to the demonstration project of small-lot NGH land transportation in western Japan. As the latest achievement, MES and Mitsui & Co., Ltd. (Mitsui) established NGH Japan Co., Ltd. (NGHJ) in April 2007, in order to study in detail on actual viability of NGH ocean transportation chain. NGHJ, MES and Mitsui have been conducting a practical feasibility study on certain cases in Southeast Asia in cooperation with 6 Japanese leading companies related to natural gas businesses. The study suggests that NGH chain was appropriate as a media for transportation from Southeast Asia to Japan and regional transportation within Southeast Asia in view of economics.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".