GreenCentre Canada: an experimental model for green chemistry commercialization
Bibliographic record
Abstract
Abstract Promising chemistry technologies are difficult to commercialize because of the “commercialization gap” that exists between academia and industry. This is especially important for discoveries in the area of green chemistry that can only fulfil their environmental and societal promise if they are successfully adopted by the chemical industry. However, the existing technology transfer model for academic commercialization is not well-suited for the highly sector-specific and long-term needs of chemistry technologies. GreenCentre Canada was founded in 2009 as a response to these commercialization needs: a chemistry-focused centre with sector-specific expertise (a Sector-specific Commercialization Centre, or SCC), including both highly trained scientists and business development professionals. GreenCentre works with academic researchers throughout Canada and internationally to evaluate, de-risk, scale-up, and optimize early-stage technologies in order to demonstrate the technology potential to industrial buyers or customers. Additionally, GreenCentre’s work extends to small- and medium-sized enterprises at a more advanced stage in the technology development process, as well as large multinational enterprises that are well-established within the chemical industry but also benefit from the centre’s expertise and resources. GreenCentre Canada represents a unique model for the development and commercialization of green chemistry technologies so that they may realize their environmental and societal benefits.
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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) | 0.000 | 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".