The Food Supply Prior to the Implementation of the Chilean Law of Food Labeling and Advertising
Bibliographic record
Abstract
This study aimed to evaluate the composition of the food supply ahead of the implementation of the Chilean Law of Food Labeling and Advertising (Law 20.606) in June 2016. The INFORMAS (International Network for Food and Obesity/Non-communicable Diseases (NCDs) Research, Monitoring and Action Support) framework for monitoring the composition of the food supply was used. The Law’s initial (2016) and final (2019) limits were used to evaluate if foods would receive a “High in” warning for Calories, Sodium, Sugars and/or Saturated Fats (initial/final, solids: >350/275 kcal; >800/400 mg; >22.5/10 g; >6/4 g; liquids: >100/70 kcal; >100/100 mg; >6/5 g; >3/3 g respectively). Foods were excluded if they required reconstitution, had missing information or if total labeled energy was estimated as incorrect (n = 942). In February 2015 and 2016, fieldworkers photographed a purposeful sample of packaged food and beverage products (n = 5421 and n = 5479) from 6 different supermarkets in Santiago, Chile. Seven percent of foods had no added critical nutrients (n = 720). Two-thirds of products had critical nutrients exceeding at least one initial limit indicative of a “high in” warning. Under the final phase limits, only 17% of foods would have zero warning labels. By 2019, 10 of the 17 food and beverage categories studied are predicted to have less than half of their products without a high in sodium warning label. While 8 of the 17 categories studied are predicted to have less than half their products without a high in total sugars or a high in total calories warning label, respectively; while even fewer food and beverage categories are predicted to be without a high in saturated fat warning label. Most products will have to be reformulated to avoid at least one front-of-package warning label.
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) | 0.000 | 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".