Converting Oil Shale to Liquid Fuels with the Alberta Taciuk Processor: Energy Inputs and Greenhouse Gas Emissions
Bibliographic record
Abstract
We calculate the greenhouse gas (GHG) emissions from producing liquid fuels from Green River oil shale with the Alberta Taciuk Processor (ATP). Kerogen contained in oil shale can be retorted to produce liquid and gaseous hydrocarbons. The ATP is an above-ground oil shale retort that combusts the coke or “char” deposited on the shale during retorting to fuel the retorting process. Using life cycle assessment (LCA), we calculate the energy inputs and outputs of each process stage. We then calculate the resulting full-fuel-cycle GHG emissions from producing reformulated gasoline using the ATP. Full-fuel-cycle GHG emissions are conservatively calculated at ≈130−150 g CO 2 equiv/MJ of gasoline produced. These emissions are 1.5 to 1.75 times larger than emissions from conventionally produced gasoline. The results depend most sensitively on the grade of shale used and the rate of carbonate mineral decomposition, which causes inorganic carbon dioxide (CO 2 ) release.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".