Carbon Dioxide Capture and Geological Storage: Contributing to Climate Change Solutions
Bibliographic record
Abstract
Abstract Concern about global climate change, and the challenges and risks it poses, will require sustained efforts to develop understanding and effective solutions while at the same time meeting the growing needs of society for energy. The development and utilization of technologies to capture and then store CO2 in underground formations offer significant potential for reducing CO2 emissions. This paper is based on the outcomes of an IPIECA workshop to advance understanding of the role of CO2 capture and geologic storage, and strategies to improve its performance and prospects. It considers CO2 capture and geological storage as a potential option for reducing future emissions of Greenhouse Gases (GHGs) from the extraction of resources, the production and use of fuels, and the generation of electricity. In doing so it examines: roles CO2 capture and geologic storage may play over the next century extending from the current assessment of this technology family; risk management to ensure safe and secure geologic storage drawing from understanding and past experiences; public perception, policy and regulatory frameworks that pose opportunities and barriers for CO2 capture and geologic storage and; initiatives and strategies to advance CO2 capture and geologic storage by reducing cost and risk, and developing sound regulatory and policy frameworks to encourage development of options for deep reductions in CO2 emissions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".