Securing Canada's future bio-based economy through genomics
Bibliographic record
Abstract
The bioproducts sector offers a unique avenue for Canada to pursue concrete environmental targets, while building a future foundation of the Canadian economy. A multitude of drivers are pointing to continued and rapid growth of the bioproducts sector in Canada and around the world, including the growing gap between demand and supply of petroleum and the increasingly unsupportable burden of greenhouse gas emissions from the production and consumption of petroleum fuels products. Governments throughout the world are investing heavily in the development of their bioproducts sectors. If Canada is to capitalize on its huge landmass and store of renewable resources, its world leading bioproducts companies, and its strengths in genomics and related science and technology areas, it must build upon the many significant federal funding programs recently put in place, and tackle areas of central concern to the Canadian bioproducts sector with a comprehensive multidisciplinary approach that includes a strong genomic component. A key step towards this goal will be the adoption by Genome Canada of the Biofuels and Bioproducts Strategic Research Theme as a priority. There is an undeniably profound relationship between genomics and bioproducts. Genomics will allow us to understand and manipulate the underlying biological processes that are the engine of the bioeconomy. While the production of a vast range of bioproducts is currently possible, only a small fraction is economically viable given existing technology. Genomics holds the promise to revolutionize the bioproducts sector by its ability to directly impact three of the biggest challenges in advancing bioproducts value chains: Area of Impact #1 - Feedstock Optimization: Significant work is required to help ensure the right feedstock is available at the right place and at the right price to support a rapidly expanding Canadian bioproducts and biofuels sector. The challenges to Canada involve both the optimization of feedstocks for the diversity of Canadian climatic regions, and the optimization of feedstock traits for industrial applications. Canada has specific research strengths, genomics and otherwise, in all three primary classes of industrial feedstocks: lignocellulosic crops, oilseeds, and cereals and grasses. Area of Impact #2 “ Microorganisms for Sustainable Processing Technologies: To achieve sustainability, the conversion of biomass into fuels and products calls for the massive deployment of novel enzymes and new fermentation processes. Genomics will greatly accelerate the identification, development and optimization of enzymes and microorganisms for a myriad of uses in the bioproducts sector, including the production of biofuels, biochemicals, and biomaterials, as well as the extraction and production of high-value bioactive compounds. Area of Impact #3 “ Value Added-Bioproducts: A key to the realization of viable biorefineries is the development of processes whereby low or negative value biomass residue from one industrial process is transformed into higher value bioproducts. Closely related is the need to identify high-value compounds from biological sources, which can anchor and make viable the production of a much larger range of bioproducts. In addition, the intersection of bioproducts and genomics raises a number of exciting research opportunities and challenges concerning various ethical, economic, environmental, social and legal issues related to sustainability. Existing Canadian bioproducts initiatives have not adequately acknowledged the role and potential of genomics, and none have exploited the broader community of genomic researchers and existing infrastructure. The lack of a unified approach to the application of genomics to the opportunities of the bioproducts sector in Canada flies in the face of efforts elsewhere, and is limiting the effectiveness and depth of the potentially huge contributions that could be made. Aggressively pursuing this research theme would fill a distinct gap in existing efforts, while simultaneously strengthening the research and commercialization efforts already underway and capturing important future international market niches for Canadian industry. Capitalizing on substantial previous investments in infrastructure and expertise, the Canadian genomic research community is poised to make significant contributions to the bioproducts sector, with multiple beneficial environmental, social, and economic outcomes. To aggressively pursue this Strategic Research Theme, it is estimated that an investment from Genome Canada of approximately $75M will be required.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".