Characterization of sites for geological storage of carbon dioxide
Bibliographic record
Abstract
Stefan Bachu is senior advisor for Energy and Carbon Management Geoscience in the Alberta Geological Survey, Alberta Energy and Utilities Board. During his career, he has been involved in various research activities related to the subsurface flow of fluids and heat, with application to the Western Canada sedimentary basin. For more than a decade, Stefan has focused his efforts on the potential for CO2 storage in geological media in Alberta as a mitigation strategy for reducing greenhouse gas emissions into the atmosphere. Because of his expertise in this emerging field, Stefan was appointed lead author and contributed to chapter 5 on CO2 Geological Storage of the IPCC Special Report on CO2 Capture and Storage . In 2004, Stefan served on the CO2 Task Force of the Interstate Oil and Gas Compact Commission and currently is a member of the Natural Sciences and Engineering Research Council (NSERC) Strategic Project Grants Panel for Greenhouse Gas Mitigation and also represents Canada on the Technical Group of the Carbon Sequestration Leadership Forum. Stefan holds advanced degrees in water resources, hydrogeology, and transport processes. Matthias Grobe is a geologist and leader of the Acid Gas and CO2 Storage Section at the Alberta Geological Survey of the Alberta Energy and Utilities Board. He received his M.Sc. degree in geology from the University of Tubingen, Germany, and his Ph.D. in geology from the University of Alberta in Edmonton, Canada, with a focus on the sedimentology and diagenesis of carbonate rocks. He considers geoscience data and knowledge as key components for the assessment of the suitability, capacity, and safety of geological storage options. Matt has been an associate editor for the journal Environmental Geosciences for several years and is currently a member of the Division of Environmental Geosciences Advisory Board. Interpretation of the …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".