A comparison of methods used to track the ‘green molecules’ and determine the carbon intensities of co‐processed fuels
Bibliographic record
Abstract
Abstract Policies such as a low carbon fuel standard (LCFS) have incentivized oil refineries to lower the carbon intensity of their operations and the fuels they produce. Although an increasing number of refineries are co‐processing biogenic feedstocks, determining the renewable content and carbon intensities of the co‐processed fuels (and potential credits earned through policies such as a LCFS) has proven to be challenging. Various methods that might be used to track the green molecules and determine the carbon intensities of co‐processed fuels were compared. Although the use of carbon 14 and mass balance have predominated, each of these methods has some advantages and disadvantages. To try to benefit from the strengths of each method, a combined direct (C14) and indirect (modified mass balance) approach was shown to give representative values. However, the quality and frequency of the information collected needs to be assured, as it can improve the quality of data needed to determine the carbon intensity of the final fuels.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".