Methodology for the economic evaluation of CO2 derived materials
Bibliographic record
Abstract
Abstract Scoring the technologies in competition for the NRG Canada’s Oil Sands Innovation Alliance Carbon XPRIZE required an economic evaluation to estimate the value created through the conversion of CO2 emissions into products. Across all of the Teams participating in the competition, 58 different materials were consumed and produced. Standardized prices and market sizes needed to be established for each of these materials to ensure a consistent evaluation across all Teams. The Standards Data Set (SDS) was created as a standardized database of economic data used in the competition. The rationale for the SDS project and the methodology for researching each material is described. Ultimately, credible material definitions using the SDS methodology were created for all materials, and some research and methodological customization were required for materials that did not have credible, publicly available market data. The methodologies for establishing credible values and market sizes for concrete, concrete admixtures and syngas are highlighted as examples of materials whose value and markets are not easily defined.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".