Additional file 1: of Consumption of low nutritive value foods and cardiometabolic risk factors among French-speaking adults from Quebec, Canada: the PREDISE study
Bibliographic record
Abstract
Table S1. Classification of some foods high in saturated fats, sugar or sodium in the R24W using the Health Canada Surveillance Tool Tier System. Table S2. Examples of calories from low nutritive value foods in mixed dishes as calculated in the R24W. Table S3. Estimation of standard deviation stratified by BMI, sex and age group among participants that came for clinical assessment. Table S4. Median intakes of low nutritive value foods among 1147 French-speaking adults from 5 administrative regions of the Province of Quebec, Canada. Table S5. “Naive” associations between incremental consumption of 250 cal from low nutritive value foods and cardiometabolic risk markers. Table S6. “Naive” associations between incremental consumption of 10% of total energy intakes from low nutritive value foods and cardiometabolic risk markers. Table S7. Associations between incremental consumption of 250 cal from low nutritive value foods and cardiometabolic risk markers, stratified by median Canadian Healthy Eating Index scores. Figure S1. Proportion of total energy intake provided by the main sources of low nutritive value foods according to sociodemographic characteristics. (PDF 337 kb)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.566 | 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 teacher head, 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".