Navigating the consumer-food interface: A regulatory perspective on plant protein in Canada
Bibliographic record
Abstract
Navigating the consumer-food interface: A regulatory perspective on plant protein in Canada Christopher P.F. Marinangeli, PhD, RD, is the Director of the Centre for Regulatory Research and Innovation at Protein Industries Canada. He discusses the consumer-food interface from a regulatory perspective on plant protein in Canada. Over the last decade, there has been considerable dialogue around multiple attributes of food system sustainability and tackling the existential and interrelated challenges of non-communicable disease and climate change. These discussions have mainly been underpinned by protein in diets, and the heavy reliance on animal-based protein foods in OECD countries. (1, 2) In response to these initiatives, there has been enhanced dialogue on the promotion of plant protein foods in developed food systems and within dietary guidelines.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".