Cold Temperature Limits to Biodiesel Use under Present and Future Climates in North America
Bibliographic record
Abstract
Cold weather operability is sometimes a limiting factor in the use of biodiesel blends for transportation. Regional temperature variability can therefore influence biodiesel adoption, with potential economic and environmental implications. This study assesses present and future biodiesel cold weather operability limits in North America according to temperature data from weather stations, atmospheric reanalysis, and global climate models with highest resolution over Ontario, Canada. Future temperature projections using the RCP8.5 climate change scenario show increases in the potential duration for certain seasonal fuel blends. For example, biodiesel blends whose cloud point temperature is -9 °C may expand their duration by 3-7% in North America for nonwinter seasons according to projections for 2040. Cloud point specifications among supply orbits in Ontario increase up to +6 °C during nonwinter seasons, with most increases observed in Fall and Spring. In winter, however, the modeling suggests no change in Ontario cloud point specifications because the coldest temperatures by mid-century are not significantly warmer than the past climate normal according to our climate simulations. This study provides a quantitative analysis on biodiesel usage scenarios under a changing climate, including Ontario region geographic temperature clusters that could prove useful for biodiesel blend-related decision-making.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".