Bibliographic record
Abstract
This reference is for an abstract only. A full paper was not submitted for this conference. The natural gas industry is undergoing significant fundamental changes as it transitions from a regional to global market. The United States hashistorically had limited interaction with the rest of the world in importing natural gas, due to sizeable North America reserves. However, as large gas producing fields mature and decline in production, while the demand for clean fuels for power generation grows, US imports outside of North America are expected to increase dramatically. Current gas imports are primarily via pipeline from Canada, however, in the future, the US is expected to import liquefied natural gas (LNG) from many parts of the world. Similarly, with growing gas demands Europe and Asia dependence on imported gas is expected to grow significantly. This will require investment in new pipeline projects to increase transit capacity for gas across adjacent regions. Also, import needs are driving huge LNG supply projects in Africa, the Middle East and Asia-Pacific. Security of gas supplies is increasingly a focus area for governments of these regions. LNG will increase linkages for natural gas around the world. Will the desired gas supply security be achieved? In this presentation of ExxonMobil's "Industry Gas Outlook", we will review the recent growth of the natural gas industry for historical context, where we are today, and the exciting challenges the global gas industry faces to find and deliver needed supplies.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.109 | 0.057 |
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".