Photographic Methods for Measuring Packaged Food and Beverage Products in Supermarkets
Bibliographic record
Abstract
The global obesity pandemic and rates of nutrition-related noncommunicable diseases (NCDs) have increased worldwide, especially in the Latin American and Caribbean region. In an attempt to control this obesity epidemic, the Chilean government has established a comprehensive set of regulatory actions, including beverage taxation, warning labels on foods, and marketing restrictions to children. To improve the effectiveness of actions to prevent obesity, a better understanding of the food environment is needed. We developed and standardized photographic methods to assess and monitor packaged food and beverage products in supermarkets. A standardized protocol and food categorization system was used to guide photo collection and data management of photos taken between February and April 2015 in 11 supermarkets, consisting of 5 different supermarket chains, from high- (n = 6) and lower-middle (n = 5)-income neighborhoods in Santiago, Chile. Photos (n = ∼50,000) from nearly 10,000 unique food products from high- and lower-middle-income neighborhoods were used for this study. We developed standardized methods to use photographs to assess and monitor the food environment. A food categorization scheme is essential to guiding the data collection process. Substantial time and human resources are required to assess packaged food and beverage products in supermarkets. Because the number of photos per food product is variable, the organization of the photographs according to the food categorization system, before data entry, is imperative for easy access during data entry and analysis. We identified the information necessary for a photographic registry, which, with the food categorization system, is critical to create unique identifiers that are linked to each food product and its photos. To adequately monitor food environments, standardized methods for food photo collection and management are essential. The information collected on food package photos to monitor food environments is important for guiding and evaluating actions in the context of the ongoing obesity and NCD epidemics.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".