Thermal Power with CCS - Classification of Cost Estimate in Context of Global CCS White Paper “Toward a Common Method of Cost Estimation for CO<sub>2</sub> Capture and Storage at Fossil Fuel Power Plants”
Bibliographic record
Abstract
This note provides a summary of how the CAPEX cost estimate provided in D4.1 maps on to the GCCSI’s proposed approach to standardise CCS cost estimates. In March 2013, the Global CCS Institute published a paper entitled “Toward a Common Method of Cost Est imation for CO2 Capture and Storage at Fossil Fuel Power Plants” (Global CCS Institute, 2013). This paper presents terminology that the Institute has put forward as a suggested standard for the categorisation of cost categories for power and CCS projects. Earlier in 2017, SNC-Lavalin produced a capital cost and operating cost estimate for the Energy Technologies Institute which contained the elements detailed in the Global CCS paper; however, categorised the costs differently. This technical note aims to identify the cost within each category as detailed in Table 4 of the Global CCS Institute paper from the SNC-Lavalin cost estimates, and identify any differences, additions, or omissions from the reference classifications.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".