Characterization of foods stored in Oaxacan and African-American households in New Brunswick, NJ
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).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".