Characterization of foods stored in Oaxacan and African-American households in New Brunswick, NJ
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Characterizing the quantity and nutritional quality of food products in consumers’ homes is important to developing programs to educate consumers on healthy dietary habits and increasing healthy food availability. It has been well-documented that the availability of healthy food directly contributes to the quality of a diet. However, obtaining an accurate picture of food stored in the home for everyday use can be extremely difficult. Self-reports by consumers and estimations derived from food-frequency questionnaires typically have significant margins of error. Traditional line-item written records have shown to be accurate but time consuming. Therefore, estimating the nutritional adequacy of household food supplies is quite difficult and new technological approaches may be warranted. Recent research comparing Universal Product Code (UPC) scanning and traditional line-item recording found that UPC scanning produced a 32% times savings while also having 95.6% accuracy.1 UPC scanning to conduct household kitchen audits is a new novel methodology that can be used to obtain an accurate picture of food stored in the home. The objective of this study is to provide an accurate assessment of the caloric and nutrient content of household food inventories of Oaxacan and African-American households and also to compare and contrast findings from previous kitchen audits conducted in a reference sample of households of varying socioeconomic status (SES).
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.
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 it