Bibliographic record
Abstract
This study focuses on an emerging application of biotechnology--Plant Molecular Farming [PMF]--which proposes to use genetically modified plant crops as production systems to produce commercially valuable biomolecules, pharmaceuticals, or industrial products, rather than to produce food, feed, and fibre. (1) The production process underlying PMF products is recombinant DNA technology, as is the case for GM food crops. Using biotechnology, a gene for a medically or industrially useful molecule is placed into a plant, creating a novel plant with new traits to produce the desired biomolecules. The products of PMF are proposed to include health and medical products (vaccines, antibodies, enzymes for cancer, diabetes and HIV), industrial products (bioenergy, biochemicals, bioplastics, personal care items, laundry detergents and cosmetic products), and agriculture and nutritional foods (disease and drought resistant crops, functional foods, nutraceuticals, etc.). (2) Arcand and Arnison (2004) speculate the potential market size of PMF may be US $10 billion by 2010, noting that there are about 34 research companies conducting PMF research and trial production, mainly located in the US and Canada. (3) Canada has three leading molecular farming research companies that may have potential in a global market (4). PMF may have many benefits, but at the same time is associated with many risks. Potential benefits include large-scale production of potential new pharmaceuticals and production of these pharmaceuticals at relatively low-cost. These two significant advantages of PMF would help to overcome current pharmaceutical production lags, which are high cost and low quantity. (5) PMF may also give low-cost and large-scale production methods to produce novel industrial products. From an industry aspect, PMF could create more jobs and be an opportunity for economic development, potentially in rural areas. However, PMF technology also raises health, environmental, social, and regulatory challenges. The risks of PMF include possible contamination of the food supply chain by cross-pollination, accidental co-mingling or disposal of waste materials. The disposal of waste materials could also cause possible contamination effects on ecosystems and the environment, as could gene flow. (6) This study undertakes socio-economic research to assess how people view the introduction and development of PMF techniques. We conducted a nation-wide online survey, drawn from a representative panel from which 1574 individuals were sampled. Among all respondents, 48.8% are male, which is representative of the gender split in Canada. From the total, 80% chose the English version and 20% chose the French version of the survey. The youngest respondent is 18 years old; the oldest respondent is 82 years old. The average age of respondents is 43.5 years old. The average household income before taxes is $50,000. The data analysis to this point is preliminary. Generally, PMF technology was not familiar to most people. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".