Bibliographic record
Abstract
Abstract Many technologies are now being explored to permit the combustion of fossil fuels while achieving CO2 capture in a state suitable for compression, transporting, and sequestration. Among the chief contenders are processes in which the fuel is first decarbonized, usually by gasification, followed by the use of a shift reaction to produce pure H2; post‐combustion capture, in which the CO2 is removed from the flue gases either at high temperatures (e.g. carbonate or Ca looping) or at near‐ambient temperatures (e.g. amine scrubbing); chemical looping in which the fuel is converted in the presence of a solid oxide carrier, thus producing a stream of gas consisting primarily of CO2 and H2O; and finally, oxyfuel combustion in which the fuel is burned in a stream of pure, or nearly pure, oxygen. The latter technology is already being investigated for application with pulverized fuel or coal, but more recently, the possibility of using oxyfuel combustion with circulating fluidized beds has been receiving increasing attention. There is already a 30 MWth demonstration unit operating in Spain, with plans to build a 300 MWe plant. This perspective describes the current status of oxyfuel research and development. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".