LCA model validation of SAGD facilities with real operation data as a collaborative example between model developers and industry
Bibliographic record
Abstract
There has been a notable disagreement between life cycle GHG emission estimates reported by research communities and key energy sector stakeholders as many LCA models are not validated against real operation data. This is originated from lack of collaboration and knowledge exchange between model developers and company experts. We present a pragmatic procedure for engaging company experts to advance the assumptions, models, and information used in an open-source LCA simulator (OPGEE). Using real operation and local emission factor data, two oil sands SAGD fields GHG emissions are compared rigorously against the scope 1 and 2 reported emissions. By introducing consistent region-specific input data, system boundaries, and assumptions, OPGEE carbon intensity estimates are within 1%-5% of reported data by companies. The system boundary expansion (e.g., expanding from direct emissions to also include offsite emissions from natural gas co-production, diluent source emission) impacts the GHG intensities estimates for both fields.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".