Bibliographic record
Abstract
Carbon capture and sequestration (CCS) is the long-term isolation of carbon dioxide from the atmosphere through physical, chemical, biological, or engineered processes. This includes a range of approaches including soil carbon sequestration (e.g., through no-till farming), terrestrial biomass sequestration (e.g., through planting forests), direct ocean injection of CO{sub 2} either onto the deep seafloor or into the intermediate depths, injection into deep geological formations, or even direct conversion of CO{sub 2} to carbonate minerals. Some of these approaches are considered geoengineering (see the appropriate chapter herein). All are considered in the 2005 special report by the Intergovernmental Panel on Climate Change (IPCC 2005). Of the range of options available, geological carbon sequestration (GCS) appears to be the most actionable and economic option for major greenhouse gas reduction in the next 10-30 years. The basis for this interest includes several factors: (1) The potential capacities are large based on initial estimates. Formal estimates for global storage potential vary substantially, but are likely to be between 800 and 3300 Gt of C (3000 and 10,000 Gt of CO{sub 2}), with significant capacity located reasonably near large point sources of the CO{sub 2}. (2) GCS can begin operations with demonstrated technology. Carbon dioxide has been separated from large point sources for nearly 100 years, and has been injected underground for over 30 years (below). (3) Testing of GCS at intermediate scale is feasible. In the US, Canada, and many industrial countries, large CO{sub 2} sources like power plants and refineries lie near prospective storage sites. These plants could be retrofit today and injection begun (while bearing in mind scientific uncertainties and unknowns). Indeed, some have, and three projects described here provide a great deal of information on the operational needs and field implementation of CCS. Part of this interest comes from several key documents written in the last three years that provide information on the status, economics, technology, and impact of CCS. These are cited throughout this text and identified as key references at the end of this manuscript. When coupled with improvements in energy efficiency, renewable energy supplies, and nuclear power, CCS help dramatically reduce current and future emissions (US CCTP 2005, MIT 2007). If CCS is not available as a carbon management option, it will be much more difficult and much more expensive to stabilize atmospheric CO{sub 2} emissions. Recent estimates put the cost of carbon abatement without CCS to be 30-80% higher that if CCS were to be available (Edmonds et al. 2004).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".