The plains CO2 reduction (PCOR) partnership - identifying CO2 sequestration opportunities for the cement industry in the central interior of North America
Bibliographic record
Abstract
The Plains CO2 Reduction (PCOR) Partnership is one of seven regional partnerships established by the U.S. Department of Energy National Energy Technology Laboratory (NETL). The goal of the NETL regional partnerships program is to assess carbon sequestration opportunities that exist throughout the United States and Canada. The PCOR Partnership region covers an area of over 1.3 million square miles and includes nine states and three Canadian provinces. During Phase I activities, an inventory was made of the region's major stationary CO/sub 2/ sources, and many of the major geologic and terrestrial sinks were identified and characterized. The most likely sequestration options were matched to the CO/sub 2/ produced by a given type of point source. Phase I activities identified thirteen cement/clinker production facilities located within the PCOR Partnership region. Collectively, they emit a total of approximately 12.5 million short tons of CO/sub 2//yr, which is 2.3% of the CO/sub 2/ emitted from point sources in the region. Amine scrubbing currently offers the best near-term potential for effective separation of CO/sub 2/ from cement kiln exit gases, with the cost of capturing and separating CO/sub 2/ from cement kiln exit gases estimated to range from $41 to $45/short ton. Compressing it to pipeline pressures costs about $9/short ton. The design and siting of cement production facilities should consider the possibility of CO/sub 2/ capture and sequestration at some point in the future. While on the surface it may seem as if capture of CO/sub 2/ from cement kilns will result in increased costs to the industry, it in fact may offer significant opportunities for development of new revenue streams, enhanced corporate image, new product development through attendant research and development, and potential efficiency gains in overall process operation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".