Energy balances of biodiesel production from soybean and canola in Canada
Bibliographic record
Abstract
Biodiesel is currently produced in Canada mostly from recycled oils and animal fats. If biodiesel is to supply 5% of diesel usage, a government objective, first-time vegetable, likely from canola and soybean, oil will also be required to provide adequate feedstocks. In this review, we estimate the life cycle energy balances for biodiesel produced from soybean and canola oil under Canadian conditions. The three broad areas of energy inputs were crop production, oil extraction, and transesterification of the vegetable oil into biodiesel. Per unit seed yield, farm production energy inputs for canola were about three times higher than for soybean, mostly because of higher nitrogen fertilizer requirements for canola. Energy required for processing and oil extraction, per unit oil, was higher for soybean. Energy allocation for co-products was allocated using a system expansion approach. Protein meal was assigned about 12% of the energy expended for canola to grow the crop and extract the oil, and about 37% for soybean. Glycerine produced during the transesterification process was allocated energy on a weight basis (11.4%). The ratio of biodiesel energy produced per energy input ranged from 2.08 t o 2.41. The energy ratio was similar for soybean and canola:soybean required less energy inputs, but also produced less oil than canola, for a given weight of seed. Key words: Biodiesel, energy, life cycle analysis, soybean, canola
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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".