CCS Activities Being Performed by the U.S. DOE
Bibliographic record
Abstract
The United States Department of Energy (DOE) is the lead federal agency for the development and deployment of carbon sequestration technologies. Its mission includes promoting scientific and technological innovations and transfer of knowledge for safe and permanent storage of CO(2) in the subsurface. To accomplish its mission, DOE is characterizing and classifying potential geologic storage reservoirs in basins throughout the U.S. and Canada, and developing best practices for project developers, to help ensure the safety of future geologic storage projects. DOE's Carbon Sequestration Program, Regional Carbon Sequestration Partnership (RCSP) Initiative, administered by the National Energy Technology Laboratory (NETL), is identifying, characterizing, and testing potential injection formations. The RCSP Initiative consists of collaborations among government, industry, universities, and international organizations. Through this collaborative effort, a series of integrated knowledge-based tools have been developed to help potential sequestration project developers. They are the Carbon Sequestration Atlas of the United States and Canada, National Carbon Sequestration Database and Geographic System (NATCARB), and best practice manuals for CCS including Depositional Reservoir Classification for CO(2); Public Outreach and Education for Carbon Storage Projects; Monitoring, Verification, and Accounting of CO(2)Stored in Deep Geologic Formation; Site Screening, Site Selection, and Initial Characterization of CO(2)Storage in Deep Geologic Formations. DOE's future research will help with refinement of these tools and additional best practice manuals (BPM) which focus on other technical aspects of project development.
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 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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".