Summary report of working group I CO{sub 2} capture, fixation/utilization, and disposal
Bibliographic record
Abstract
The topics of our working group were divided into four key areas: CO{sub 2} Capture, Utilization/Fixation, Ocean Disposal, and Land Disposal. Fourteen presentations were made as follows: CO{sub 2} Capture: Toshikatsu Hakuta (Japan) and Rod Judkins, Bruce St. John, and Alan Wolsky (US). Utilization/Fixation: Hironori Arakawa, Yasuo Asada, and Takashi lbusuki (Japan) and Ed Lipinsky (US). Ocean Disposal: Yuji Shindo (Japan) and Eric Adams, Gerard Nihous, and Wheeler North (US). Land Disposal: Shoichi Tanaka (Japan) and Roger Bailey (US/Canada). Co-chairs for this working group were Toshikatsu Hakuta (Japan) and Howard Herzog (US). This document contains only a summary outline of research needs in the area of CO{sub 2} capture and sequestration. It should be used in conjunction with other assessments made in this area. For the U.S., a DOE report entitled A Research Needs Assessment for the Capture, Utilization and Disposal of Carbon Dioxide from Fossil Fuel-Fired Power Plants will be forthcoming in 1993.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.049 |
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".