Potential supplies of fuel-grade canola oil for low-carbon fuel production in Alberta, Canada: GIS analysis using an improved service-area approach
Bibliographic record
Abstract
Abstract Our study investigates prospects for a fuel-grade canola oil supply chain that prioritizes the use of non-No. 1 Canola as a biofuel feedstock. Using low-grade canola oil to produce biofuels can reduce feedstock costs and offers the opportunity to utilize existing petroleum infrastructure to transport and store canola oil, thereby reducing capital costs for biorefinery investments. We conduct a township-level GIS analysis to assess the availability of canola seed in Alberta and identify potential fuel-grade crushing sites based on the amount of annually accessible non-No. 1 Canola. Using an improved service-area approach, we identify three potential sites for fuel-grade crushers, all of which had sufficient seed to produce, on average, over 65 thousand tonnes of oil per year (from 2016–2019). Northwestern Alberta appears to be especially suitable for a fuel-grade canola crushing plant, since it has the highest average amounts of non-No. 1 seed, and there are no existing food-grade crushers to compete with. Results further show that spatial and temporal variation in canola harvests impacts how much non-No.1 seed is available, and could therefore influence investment decisions on where to locate fuel-grade canola crushing plants. New fuel-grade crushing plants could also stimulate regional development and boost incomes for local canola producers. Our analysis is relevant to policy and business decisions related to fuel-grade canola oil supply chain investments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".