Opportunities for the expansion of ethanol production in western Canada
Bibliographic record
Abstract
A renewed interest in ethanol production in North America is occurring because of: (1) higher gasoline prices which have improved the viability of ethanol investments; (2) commitments by Canada, the U.S., and other nations to reduce greenhouse gas emissions; (3) opportunities for rural employment through additional value added industry; and (4) a desire to reduce burdensome grain stocks and thus assist in the ultimate recovery of grain prices benefitting all agricultural producers. Changes in transportation costs for export grain in Western Canada provide a further incentive promoting the ethanol industry. This paper summarizes some of the developments and issues relating to the expansion of ethanol production in Western Canada. Among the issues for those interested in ethanol have been: (1) Is this industry likely to be viable and to compete with other fuel sources, particularly gasoline? (2) Does the industry require ongoing incentive schemes in order to compete? (3) Does the industry require additional incentives in order to assist Canada in reducing greenhouse gas emissions? (4) How effective is ethanol in reducing greenhouse gas emissions?, and (5) Is the expansion of ethanol production effective in reducing grain carryovers to help stabilize grain prices? None of these questions have complete or absolute answers, but additional research and experience is shedding light on these issues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".