Science Across Borders: 5th Annual Natural Health Product Research Conference—March 26–29, 2008, Toronto, Canada
Bibliographic record
Abstract
Canada is experiencing a growing interest in the use of alternative therapies and products particularly natural health products (NHP). In 1997, Canadians spent around C$ 2 billion on NHP. In an attempt to catch with this popularity of NHP use, Canadian researchers and administrators from academia, industry and government jointly established the Natural Health Product Research Society of Canada (NHPRS). Since its formation, NHPRS has been organizing an annual meeting which brings together world renowned researchers and experts in the area of NHP research. For 2008, the annual NHPRS meeting took place in Toronto from the 26th to 29th of March with a focus on 'Science Across Borders: Global Natural Health Products Research'. The scientific program was spread into three days of plenary lectures and oral presentations. The different sessions containing these talks were on: ethnobotany around the world; chemical analysis of NHP; product standards and quality control; ethnomedicine; novel analytical approaches; systemic research, nutrisciences and molecular medicine; and drug development from NHP. The meeting proved to be a great success in terms of the speakers that were invited and based on the data that was presented which highlighted recent research taking place in the field of NHP not only in Canada but from many parts of the world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.015 |
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".