Evaluation of Children’s Lunch Box Contents by Photograph and Their Relationship with Mothers’ Concern
Bibliographic record
Abstract
Japanese kindergarten children usually bring lunch prepared by mothers. The contents may be influenced by mothers’ food concerns. We investigated the relationship between mothers’ concerns and children’s lunch box contents and preferences. Lunch boxes of 209 children were digitally photographed for 4 days at a private kindergarten in Japan. The amounts of rice, main dishes, vegetables and fruits in the lunch boxes were estimated by measuring the area occupied by each in the photograph; a questionnaire, including questions on mothers’ concerns and children’s preferences, was completed by mothers. Vegetable amounts in the lunch boxes were significantly related to mother’s concerns for their children’s lunch. Compared with estimated vegetable amounts below 11%, the amounts above 11% indicated that the number of foods disliked by children was lower, and mothers reported a higher rate of mindfulness towards vegetables and lower rate towards frozen food and believed that they prepared a balanced lunch. Thus, vegetable amounts in children’s lunch boxes, estimated using photographs, may predict mothers’ food concerns and children’s balanced/unbalanced diets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".