Lipids to biojet and sustainable aviation fuel: uses and competing demands for lipid feedstocks
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
Abstract The use of biojet and sustainable aviation fuels (SAF) as low carbon‐intensity (CI) fuels is likely to be the primary pathway to decarbonize aviation. ‘Waste lipids’, such as fats, oils, and greases (FOGs), are preferred feedstocks due to their lower CI. However, they are mainly used to make biodiesel or renewable diesel. Therefore, there has been a significant increase in the price paid for “waste lipids” and consequently an increase in their collection. However, due to the limited availability and competition, waste‐lipid/FOG feedstocks are unlikely to meet 2030 demands for biojet or SAF. Consequently, increasing amounts of SAF are produced from higher CI plant‐derived lipids such as soya and rape/canola. Currently, about 20% of the world’s vegetable‐derived lipids are used to make biodiesel or renewable diesel and SAF, with the majority allocated to food and oleochemicals. The rising value and scarcity of waste‐lipid/FOG feedstocks, coupled with competition from both traditional markets (e.g., edible oil, cosmetics) and biofuels has heightened interest in lipid feedstocks. Greater attention will be directed towards the CI of lipids, if the overall objective is to decarbonize the fuel. However, biodiesel/renewable diesel benefits from lower costs, increased yields, and lower CI than biojet/SAF. This will result in increasing competition for lipid feedstocks between the biodiesel, renewable diesel, and biojet/SAF markets.
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 | 0.000 | 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 | 0.000 |
| 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 it