Bibliographic record
Abstract
Society Executive: President – Paula N. Brown, PhD, Canada Research Chair & Director, Natural Health and Food Products Research Group (NRG), British Columbia Institute of Technology, Burnaby, British Columbia, Canada Vice-President – Bob Chapman, PhD, Principal Research Officer at National Research Council of Canada, Charlottetown, Prince Edward Island, Canada Treasurer – Susan J. Murch, PhD, Chemistry, University of British Columbia, Kelowna, British Columbia, Canada Secretary – Pamela Ovadje, PhD, Research & Regulatory Affairs, Advanced Orthomolecular Research, Calgary, Alberta, Canada Conference Co-Chair – Paul Spagnuolo, PhD, Department of Food Science, University of Guelph, Guelph, Ontario, Canada Conference Co-Chair – Krista Coventry, Director of Regulatory Services (East), Source Nutraceutical Inc., Winnipeg, Manitoba, Canada About the Society The Natural Health Product Research Society of Canada (NHPRS) is a Canadian federally incorporated non-profit organization founded in 2003 by a collaboration of academic, industry, and government researchers from across Canada. The goals of the NHPRS are; (a) to promote scientifically rigorous research and education on Natural Health Products (NHPs), (b) to develop a national research community that encompasses academic, health professional, government anda broad-base of industry stakeholders, (c) to support national research priorities that best enable the informed and appropriate use of NHPs that are safe and efficacious, (d) to increase the capacity for NHP research and education, (e) to facilitate effective NHP knowledge transfer and translation, (f) to support the use of science-based product quality standards and the use of well-characterized materials and protocols in research and (g) to foster value-chain development through interdisciplinary NHP research collaborations and networking. Since 2003, the NHPRS has held an annual research conference with workshops to explore emerging issues in the NHP community. The theme of the 15 th Annual Conference is “Innovation at the NHP/Food Interface”. The scientific program is designed to evoke discussion and comtempation as it presents novel approaches, technologies and ideas to complement current practices for the development, use, authentication, study and regulation of NHPs.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.056 | 0.050 |
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".