The First North American Carbon Storage Atlas
Bibliographic record
Abstract
Canada, Mexico and the United States formed the North American Carbon Atlas Partnership (NACAP) in December 2008 to collaborate in the development of a North American Carbon Storage Atlas (NACSA). This partnership was formally announced by the Presidents of the United States and Mexico and the Prime Minister of Canada at their meeting in Guadalajara, Mexico, in August 2009. The NACAP effort identified and quantified large stationary sources of carbon dioxide (CO 2 ) emissions, identified and screened sedimentary basins suitable for CO 2 storage, and estimated the CO 2 storage resources of the three most common types of geological media—oil and gas reservoirs, unmineable coal and deep saline formations—in those basins using publicly available geological data. To develop the atlas NACAP had to harmonize storage resource estimation methodologies, define a common scale and resolution, and develop procedures for the treatment of shared sedimentary basins across national borders. Although North America is a large emitter of CO 2 , the results of the assessments by the three countries demonstrate that potential CO 2 storage resources in North America are hundreds, if not thousands, of times greater than current CO 2 emissions. Certainly, practical considerations will reduce these estimates. The maps of the large stationary CO 2 sources and of the CO 2 storage resources show that the sources and storage resources frequently either overlay each other or are within manageable distances of each other, making carbon capture and storage an attractive option to reduce CO 2 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".