Additional file 1 of The relationship between voluntary product (re) formulation commitments and changes in the nutritional quality of products offered by the top packaged food and beverage companies in Canada from 2013 to 2017
Bibliographic record
Abstract
Additional file 1: Supplementary Table 1. Weighted Food Company Reformulation tool scores of the top packaged food and beverage companies in Canada. Supplementary Table 2. Absolute and percentage changes in mean Health Star Ratings and median calories, sodium, saturated fat, trans fat, total sugars and free sugars per 100 g (or mL) from 2013 to 2017 in the total portfolio of products offered by each company, presented by food category. Supplementary Table 3. Mean and median absolute and percentage changes in Health Star Ratings, calories, sodium, saturated fat, trans fat, total sugars and free sugars per 100 g (or mL) in products offered by each company that were matched between 2013 and 2017, presented by food category. Supplementary Table 4. Mean Health Star Ratings and median amounts of calories, sodium, saturated fat, trans fat, total sugars and free sugars per 100 g (or mL) in products offered by each company in 2013, presented overall and by food category. Supplementary Fig. 1. The approach used to derive the sample of products examined in this study.
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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.778 | 0.105 |
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".