A New Classification System For Evaluating CO2 Storage Resource/Capacity Estimates
Bibliographic record
Abstract
Abstract Carbon dioxide (CO2) storage estimates are a critical component of the decision-making process when considering the implementation of large-scale CO2 storage in the subsurface. In order to compare storage resource/capacity estimates, both the scale of the estimate and the type of estimate must be considered. To date, the classification of resources and commodities has been used almost exclusively for valuable materials that can be economically extracted from the subsurface, e.g., hydrocarbons, metal ore, coal, etc. These industries have benefited from the establishment of classification systems with consistent terms and definitions that have gained international acceptance and allow for systematic accounting and comparison of resources across geological, geographical, and jurisdictional boundaries. As the carbon capture and storage (CCS) industry grows, there is increasing need for an accepted classification system that describes the available CO2 storage resource. While the classification systems used in the mining and hydrocarbon industries have elements that are instructive and sometimes indirectly applicable with respect to CO2, the direct application of those systems is largely insufficient. This is because, in the context of geological CO2 storage, the desired resource is not something to be removed from a subsurface reservoir but rather the accessible pore volume of the reservoir itself. Although much work has been accomplished, particularly by the Carbon Sequestration Leadership Forum (CSLF), and the U.S. Department of Energy (DOE) in the Carbon Sequestration Atlas of the United States and Canada, inconsistencies in definitions related to CCS exist between groups, and a widely accepted set of definitions for discussing CO2 storage resource and capacity has not yet been established. In order to move the CCS industry toward a useful set of definitions and provide a consistent set of terms, an improved classification system has been developed to not only address the level of the assessment but also the scale at which the assessment was made.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".