Model to Investigate Energy and Greenhouse Gas Emissions Implications of Refining Petroleum: Impacts of Crude Quality and Refinery Configuration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A petroleum refinery model, Petroleum Refinery Life-cycle Inventory Model (PRELIM), which quantifies energy use and greenhouse gas (GHG) emissions with the detail and transparency sufficient to inform policy analysis is developed. PRELIM improves on prior models by representing a more comprehensive range of crude oil quality and refinery configuration, using publicly available information, and supported by refinery operating data and experts' input. The potential use of PRELIM is demonstrated through a scenario analysis to explore the implications of processing crudes of different qualities, with a focus on oil sands products, in different refinery configurations. The variability in GHG emissions estimates resulting from all cases considered in the model application shows differences of up to 14 g CO₂eq/MJ of crude, or up to 11 g CO₂eq/MJ of gasoline and 19 g CO₂eq/MJ of diesel (the margin of deviation in the emissions estimates is roughly 10%). This variability is comparable to the magnitude of upstream emissions and therefore has implications for both policy and mitigation of GHG emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it