The Value of a New Biotechnology Considering R&D Investment and Regulatory Issues
Bibliographic record
Abstract
The prevalence of the products of biotechnology in Canada's canola industry is vast. More than 95% of Canada's seeded area is in herbicide-tolerant (HT) varieties, which are products of biotechnology. Overall, the industry has experienced significant growth; for instance, the area seeded to canola varieties has increased from less than one million hectares (ha) in the 1960s to over 8 million today (Statistics Canada, n.d.). Later in the article we show that the number of commercial varieties available and the index of yields for those varieties have increased sharply since the 1980s (Brewin & Malla, 2012; Phillips, 2001). Agricultural biotechnology could facilitate further productivity growth in crops such as canola. However, the policies and regulations that are in place might need to further evolve to insure continuing growth in the sector. Furthermore, assessing the benefits to Canadian producers by adopting HT and hybrid varieties over time would improve our understanding of the sector and the gains that are possible under comparable regulations for similar sectors. A significant portion of these benefits were facilitated by changing the institutions in Canada to provide incentives to private investment.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".