Co-operatives at the University of Saskatchewan. © 1999 AgBioForum. THE PRODUCER BENEFITS OF HERBICIDE-RESISTANT CANOLA
Bibliographic record
Abstract
The commercial sale of herbicide resistant (HR) canola has raised questions about the benefits of this new technology for seed and chemical companies, farmers, consumers, and other players in the supply chain. In this paper, we argue that the pricing and adoption of HR canola in Canada can not be understood if producers are seen as being homogeneous. We develop a conceptual model of producer heterogeneity that represents the distribution of benefits among producers. In this context, some farmers benefit from the new technology leading to adoption, while others do not. Empirical evidence supports this argument. In addition, the new technology co-exists with the traditional technology. Key words: herbicide resistant canola; producer heterogeneity; benefits; new technology; biotechnology. Canola is one of the first genetically modified (GM) crops to reach the commercial market in Canada. The focus of genetic modification is the addition of herbicide resistance to canola pl ts. In 1998, after four years of production, herbicide-resistant (HR) canola comprised 44 percent of total canola production in Canada (see table 1). One source estimates that Canadian HR c nol production could reach as high as 70 percent of total production in 1999 (Plant Breeding Institute (PBI), 1998). Table 1. Acres of Herbicide-Resistant C ola in Canada (‘000s acres).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.823 | 0.589 |
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".