Regulating the Geological Sequestration of CO<sub>2</sub>
Bibliographic record
Abstract
Governments worldwide should provide incentives for initial large-scale GS projects to help build the knowledge base for a mature, internationally harmonized GS regulatory framework. Health, safety, and environmental risks of these early projects can be managed through modifications of existing regulations in the EU, Australia, Canada, and the U.S. An institutional mechanism, such as the proposed Federal Carbon Sequestration Commission in the U.S., should gather data from these early projects and combine them with factors such as GS industrial organization and climate regime requirements to create an efficient and adaptive regulatory framework suited to large-scale deployment. Mechanisms to structure long-term liability and fund long-term postclosure care must be developed, most likely at the national level, to equitably balance the risks and benefits of this important climate change mitigation technology. We need to do this right. During the initial field experiences, a single major accident, resulting from inadequate regulatory oversight, anywhere in the world, could seriously endanger the future viability of GS. That, in turn, could make it next to impossible to achieve the needed dramatic global reductions in CO2 emissions over the next several decades. We also need to do it quickly. Emissions are going up, the climate is changing, and impacts are growing. The need for safe and effective CO2 capture with deep GS is urgent.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".