Health Canadaâs Proposal to Accept a Health Claim about Soy Products and Cholesterol Lowering
Bibliographic record
Abstract
Health Canadaâs Food Directorate is making available this paper, following an internal peer review by Food Directorate\nscientific and regulatory experts, to seek comments from peer scientists, regulators and stakeholders prior to finalization.\nThis paper is open for comment commencing October 22, 2014, and closing on November 21, 2014 (30 calendar days).\nComments of a scientific nature only will be considered in developing the final version of this document. Authors will strive\nto document how the various comments received, when deemed relevant, were considered in amending and shaping the\nfinal published document.\nComments may be submitted electronically at the address indicated below. Please use the phrase âSoy Health Claim Commentsâ\nin the subject box of your e-mail.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.025 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.038 |
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".