PLAINS CO2 REDUCTION PARTNERSHIP
Bibliographic record
Abstract
The Plains CO{sub 2} Reduction (PCOR) Partnership continues to make great progress. Task 2 (Technology Deployment) focused on developing information regarding deployment issues to support Task 5 (Modeling and Phase II Action Plans) and provided information to be used to assess CO{sub 2} sequestration opportunities in the PCOR Partnership region. Task 2 efforts also included preparation of a draft topical report entitled ''Deployment Issues Related to Geologic CO{sub 2} Sequestration in the PCOR Partnership Region'', which is nearing completion. Task 3 (Public Outreach) focused on developing an informational video about CO{sub 2} sequestration. The video will be completed and aired on Prairie Public Television in the next quarter. Progress in Task 4 (Sources, Sinks, and Infrastructure) included the continued collection of data regarding CO{sub 2} sources and sinks and data on the performance and costs for CO{sub 2} separation, capture, treatment, and compression for pipeline transportation. The addition of the Canadian province of Alberta to the PCOR Partnership region expanded the decision support system (DSS) geographic information system database. Task 5 screened and qualitatively assessed sequestration options. Task 5 activities also continue to be useful in structuring data collection and other activities in Tasks 2, 3, and 5.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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".