Assessing the commercialization potential of algal jet fuel using a lifecycle assessment approach
Bibliographic record
Abstract
Farming algae for chemicals, pigments, neutraceutical and even fuel is not a novel idea. What is new however is recent volatility in energy prices coupled with heightened global sensitivity to food prices - partly instigated by the massive proliferation of food-based biofuels - that has brought algal biofuels to the forefront of energy research and commercial activity. Algal biofuels offer great promise in providing a sustainably-sourced, carbon-neutral option that can meet a significant portion, if not all, of the global transportation fuel needs in the coming decades. For a sector such as aviation, which has no other short-term practical alternative to fossil fuel liquids fuels, algal jet fuel offers a massive opportunity that if captured, can provide fuel cost and supply stability as well as a critical avenue to actively manage its growing GHG footprint. However, being a nascent technology, the fuel pathway innovation will rely on heavy and continuous investment to accelerate its development. This study assesses whether a carbon price framework can enhance the commercialization potential of algal jet fuel by way of mobilizing investment into the technology, and if not, what requisite improvements in technology and policy accommodations need to be made in order to allow algal jet fuel to be competitively produced.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".