Monetizing Gas: Focusing on Developments in Gas Hydrate as a Mode of Transportation
Bibliographic record
Abstract
Natural gas and energy resource management is a major challenge in the rapidly changing global and business environment. Increase in gas recoveries and gas production have led a major review in the ways of transporting natural gas energy. Monetizing gas has now become a high priority issue for many countries. Natural gas is a much cleaner fuel than oil and coal especially for electricity generation.Interest in gas hydrate being used as a means of transporting natural gas has increased over the last decade. New technology development has been focusing on using gas hydrates as a way of converting gas to solids to transport to markets around the world. Gas hydrate may be a viable means of storing and transporting gas but more focus should be given to some critical considerations for this gas hydrate development.This paper would discuss some of these issues as we move towards monetizing gas in the form of hydrate. These include energy balance in hydrate formation and re-gasification, storage of the hydrate, form of transporting the hydrate and distances to be transported. Other important factors are re-gasification technologies, economics compared to other gas transportation modes, environmental, climate and other issues.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".