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
Summary While remote parts of the world are awash with hundreds of trillions of cubic feet (Tcf) of natural gas, the industrialized West and emerging economies of the East cannot get enough of the clean-burning, environmentally friendly fuel. The problem is transporting this compressible fluid long distances and across major bodies of water. For markets more than 1,500 miles distant, liquefied natural gas (LNG) has proved to be the most economic option. By refrigerating natural gas (primarily methane) to–260°F (–162°C), thereby shrinking its volume by 600:1, natural gas in the form of LNG can be transported in large insulated cryogenic tankers at a reasonable cost. Natural-gas liquefaction is a series of refrigeration systems similar to home air-conditioning (AC) systems, consisting of a compressor, condenser, and evaporator to chill and condense the gas. The difference is in the scale and magnitude of the refrigeration. A typical single-train LNG plant may cost USD 1.5 billion and consume 6 to 8% of the inlet gas as fuel. Because many of the impurities (e.g., water vapor, carbon dioxide, hydrogen sulfide) and heavier hydrocarbon compounds in natural gas would freeze at LNG temperatures, they must first be removed and disposed of or marketed as separate products. This paper will provide an overview of LNG liquefaction facilities, from inlet gas receiving to LNG storage and loading. However, the focus is on the liquefaction process and equipment. Differences among the commercially available liquefaction processes (e.g., cascade, single mixed refrigerant, propane precooled mixed refrigerant, double-mixed refrigerant, nitrogen) will be discussed. The aim is to provide SPE members with a clear understanding of the technologies, equipment, and process choices required for a successful LNG project.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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