Bibliographic record
Abstract
Natural Products Canada (NPC) is a unique addition to Canada's innovation scene. It works with a diverse array of partners to support the commercialization of naturally-derived products and technologies in life sciences, natural resources, and environmental sustainability. This has become known as the natural product commercialization ecosystem. Shelley King will outline the key components of the ecosystem, and NPC's role as connector and enabler of this vast and multifaceted system. Ms. King will provide examples of the work NPC conducts to help bring products to market as it relates to four key pillars: CONNECT Linking research institutions, companies, investors, and others to foster a thriving commercialization ecosystem. EVALUATE Enabling timely and accurate assessment of products, technologies, and market opportunities. ACCELERATE Helping researchers, research institutions, and companies access the right experts and resources to overcome commercialization obstacles as quickly and efficiently as possible. INVEST Encouraging investment in promising opportunities through introductions, technical evaluation and due diligence, and where appropriate, co-investment. NPC was established in 2016 and is funded by a range of public and private investors, including the Government of Canada's Centre of Excellence in Commercialization and Research (CECR) program, administered by the Networks of Centres of Excellence. Visit www.naturalproductscanada.com.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".