Considerations for monitoring, verification, and accounting for geologic storage of CO2
Bibliographic record
Abstract
Growing concern over the impact of increasing concentrations of greenhouse gases (GHGs), especially carbon dioxide (CO 2 ), in the atmosphere has led to suggested mitigation techniques. One proposal that is attracting widespread attention is carbon capture and storage (CCS). This mitigation approach involves capture of CO 2 and permanent storage in geologic formations, such as oil and gas reservoirs, deep saline formations, and unmineable coal seams. Critical to the successful implementation of this approach is the development of a robust monitoring, verification, and accounting (MVA) program. Defining the site characteristics of a proposed geologic storage project is the first step in developing a monitoring program. Following site characterization, the second step involves developing hypothetical models describing important mechanisms that control the behavior of injected CO 2 . A wide array of advanced monitoring technologies is currently being evaluated by the Weyburn―Midale Project, the Frio Project, and the U.S. Department of Energy's Regional Carbon Sequestration Partnerships Program. These efforts are evaluating and determining which monitoring techniques are most effective and economic for specific geologic situations, information that will be vital in guiding future projects. Although monitoring costs can run into millions of dollars, they are typically only a small part of the overall cost of a CO 2 storage project. Ultimately, a robust MVA program will be critical in establishing CCS as a viable GHG mitigation strategy.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".