Comparative analysis of producer and processor impacts from bioproduct developments in the Canadian soybean sector
Bibliographic record
Abstract
Volatility in crude oil prices has spurred new interest in the nontraditional use of agricultural products in the production of biofuels and in other industrial applications such as the Ontario BioCar initiative. It is important to know if these new demands will provide benefits to Ontario farmers. A synthetic structural model that depicts Canada as part of world corn and soybean markets was used to answer this question through a comparative static analysis of how the benefits derived from the biotech demands for corn and soybean oil compare to the benefits that could be obtained from an increase in soybean productivity or from an increase in the export demand for Canadian food grade soybeans. It was found that Canadian farmers see producer surplus improvements from increases in corn ethanol demand, despite the increasing use of distillers dried grains, if they are willing to grow corn at the expense of soybeans. It was also discovered that Canadian soybean growers have little to gain from the development of industrial applications for soybean oil, such as the BioCar project, but that they do benefit when the demand for soybean oil stems from the US and the rest of the world. Soybean productivity improvements created soybean producer surplus losses when they were applied globally, but were beneficial when they were proprietary to Canada. An increase in the export demand for Canadian food grade soybeans provided substantial benefits to food bean growers and more modest producer surplus losses for crush bean growers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".