National Carbon Sequestration Database and Geographic Information System (NatCarb)
Bibliographic record
Abstract
This annual and final report describes the results of the multi-year project entitled 'NATional CARBon Sequestration Database and Geographic Information System (NatCarb)' (http://www.natcarb.org). The original project assembled a consortium of five states (Indiana, Illinois, Kansas, Kentucky and Ohio) in the midcontinent of the United States (MIDCARB) to construct an online distributed Relational Database Management System (RDBMS) and Geographic Information System (GIS) covering aspects of carbon dioxide (CO{sub 2}) geologic sequestration. The NatCarb system built on the technology developed in the initial MIDCARB effort. The NatCarb project linked the GIS information of the Regional Carbon Sequestration Partnerships (RCSPs) into a coordinated regional database system consisting of datasets useful to industry, regulators and the public. The project includes access to national databases and GIS layers maintained by the NatCarb group (e.g., brine geochemistry) and publicly accessible servers (e.g., USGS, and Geography Network) into a single system where data are maintained and enhanced at the local level, but are accessed and assembled through a single Web portal to facilitate query, assembly, analysis and display. This project improves the flow of data across servers and increases the amount and quality of available digital data. The purpose of NatCarb is to provide a national view of the carbon capture and storage potential in the U.S. and Canada. The digital spatial database allows users to estimate the amount of CO{sub 2} emitted by sources (such as power plants, refineries and other fossil-fuel-consuming industries) in relation to geologic formations that can provide safe, secure storage sites over long periods of time. The NatCarb project worked to provide all stakeholders with improved online tools for the display and analysis of CO{sub 2} carbon capture and storage data through a single website portal (http://www.natcarb.org/). While the external project is ending, NatCarb will continue as an internal US Department of Energy National Energy Technology Laboratory (NETL) project with the continued cooperation of personnel at both West Virginia University and the Kansas Geological Survey. The successor project will continue to organize and enhance the information about CO{sub 2} sources and developing the technology needed to access, query, analyze, display, and distribute natural resource data critical to carbon management. Data are generated, maintained and enhanced locally at the RCSP level, or at the national level in specialized data warehouses, and assembled, accessed, and analyzed in real-time through a single geoportal. To address the broader needs of a spectrum of users form high-end technical queries to the general public, NatCarb will be moving to an improved and simplified display for the general public using readily available web tools such as Google Earth{trademark} and Google Maps{trademark}. The goal is for NatCarb to expand in terms of technology and areal coverage and remain the premier functional demonstration of distributed data-management systems that cross the boundaries between institutions and geographic areas, and forms the foundation of a functioning carbon cyber-infrastructure. NatCarb provides access to first-order information to evaluate the costs, economic potential and societal issues of CO{sub 2} capture and storage, including public perception and regulatory aspects.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.077 |
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".