Potential ethanol biorefinery sites based on agricultural residues in Alberta, Canada: A GIS approach with feedstock variability
Bibliographic record
Abstract
Though numerous studies have investigated optimal locations for biorefineries given the availability of feedstock supplies, few have considered that these supplies could vary over time, thereby potentially failing to meet feedstock requirements. In this paper, we develop a process for identifying a shortlist of five potentially viable biorefinery sites in Alberta, and then consider the stochastic nature of these supplies, and what that variation could mean for supplying a biorefinery. A shortlist of sites was selected based on agricultural residues within a distance of 80 km of the site. Across all sites, a total amount of 16.7 million t [oven dried] y−1 is found to be economically accessible in the province. However, this shortlist is based only on a summation of one-year crop yields surrounding the sites. When crop yield variability (estimated from data over 40 years between 1978 and 2017) is also considered, substantially different results were found. For example, if supply was required to exceed 1.59 million t [oven dried] y−1, 95% of the time, then two selected sites, which were chosen based on one-year crop yields, must be excluded. Under these circumstances the total amount available across sites falls to 12.4 million t [oven dried] y−1. Overall, when variability in supply is included as a criterion, site selection and rankings change, suggesting that site selection using only an average or point estimate is not sufficient. Instead, more sophisticated techniques to investigate feedstock supply variability would be useful.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".