Biodiesel from Oilseeds in the Canadian Prairies and Supply-Chain Models for Exploring Production Cost Scenarios: A Review
Bibliographic record
Abstract
Canada recently implemented a federal mandate of 2% of renewable content in diesel fuel and heating oil. Federal-level biofuel strategy is currently more geared to bioethanol, as nonfood oils continue to be more cost-competitive and canola seeded area is forecast to increase 10% as a new record due to strong prices and high expected yields. Increasing focus is therefore being placed on alternative oilseeds as nonfood crops for biodiesel and their ability to adapt to the semiarid conditions of the Canadian Prairies and provide benefits in nutrient and water-use efficiency when introduced into the crop rotation. Systems engineering and supply-chain modeling and optimization will have an increasingly important role in decision making for designating supply units, the linkage of processes and chains, and biorefinery system design. However, current models require further enhancement to address current challenging questions: (1) changing spatial considerations (e.g., land use and suitability for feedstocks), (2) changing temporal dynamics of supply and risk of climate extreme impacts on transportation networks (road, rail, pipeline), price volatility, changes in policy targets and subsidy regimes, process technological change, and multigenerational biorefinery systems engineering advancements. Greater integration internationally in model development and testing would improve sensitivity and reliability in their system-level predictions and forecasts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".