Accelerating Carbon Capture and Storage Implementation in Alberta Alberta Carbon Capture and Storage Development Council
Bibliographic record
Abstract
Accelerating Carbon Capture and Storage Implementation in Alberta, a blueprint to achieving swift, safe and widespread adoption of carbon capture and storage (CCS) in Alberta. With the 2008 announcement of a $2-billion CCS program, Alberta has once again tapped its pioneering roots and has assumed a globally leading role in the development of CCS technology. The projects that will result from this program will create important momentum. Now is the time to prepare to capitalize on that momentum. Our report confirms the potential of CCS to make a meaningful impact on greenhouse gas (GHG) emissions, and it sets forth the path we believe we must follow to make this potential a reality. Developing and implementing CCS technology is a tremendous opportunity for Alberta and for Albertans. CCS funding is an investment in the environment because it will significantly reduce GHG emissions. CCS funding, by both government and industry, is equally an investment in the economy because it ensures that GHG-emitting industries remain competitive on the international stage and it spurs a wealth of growth opportunities that Albertans are uniquely positioned to capitalize upon. Ultimately, CCS will be one of the keys to sustaining and building upon the standard of living that Albertans have earned
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".