The PortionSize Ed Mobile App Demonstrated Feasibility, Acceptability, and Preliminary Efficacy for Improving Diet Quality Among Early Adolescents in SNAP-Ed Hawaii
Bibliographic record
Abstract
Results: A total of 22 child-directed marketing techniques featured were identified and annotated.The CNN model outperformed kNN and SVM, achieved 0.92 accuracy and 0.98 AUC in identifying child-targeted marketing on food packaging.YOLOv10 object detection model showed varied performance across child-directed marketing features.Among breakfast cereals, 35.1% were identified to display child-directed marketing features that would be restricted under Canadian proposed marketing-to-children regulations.Products with child-directed marketing features tended to be less expensive, though not significant (p0.072).Conclusions: This study introduces an AI-driven strategy for identifying and categorizing child-directed marketing on food packaging, with potential extensions to social media and online video content for broader applications.These scalable methods support efficient monitoring of compliance with marketing-tochildren policies and provide evidence-based insights to reduce children's exposure to unhealthy food marketing to improve health outcomes.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".