Decarbonizing British Columbia's (<scp>BC</scp>'s) marine sector by using low carbon intensive (<scp>CI</scp>) biofuels
Bibliographic record
Abstract
Abstract The long‐distance transport sector will not be easily electrified. Aviation will primarily use sustainable aviation fuels (SAF) to meet its decarbonization targets. Although marine will also be hard to decarbonize this sector has several lower carbon‐intensive (CI) fuel options such as electric‐hybrids, liquefied natural gas (LNG), ‘green’ methanol, ammonia, hydrogen, and biobased fuels. The advantages of using these latter fuels are their ready integration into much of the existing infrastructure, their availability, their use in today's marine engines, substantial carbon emission reductions, and established supply chains. However, there will be increasing competition for the oleochemical/lipid feedstocks from the trucking and aviation sector, while the cost, availability, and overall sustainability of these biofuels are problematic. In the longer term, it is hoped that biomass‐derived biocrudes produced via thermochemical processes such as pyrolysis, hydrothermal liquefaction (HTL) and gasification will supplement the lipid feedstocks and be more sustainable, cheaper, and plentiful. The robustness of marine engines is also conducive to the direct use of straight vegetable oils (SVO) and biocrudes. However, ‘enabling’ policies such as the British Columbia low carbon fuels standard will be required to bridge the initial price gap between fossil and low‐CI fuels while the international nature of most marine traffic will require agreement on how the life cycle analysis (LCA) is determined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".