Bibliographic record
Abstract
This paper presents an AI-enabled nutrition coach that combines computer vision with reinforcement learning (RL) to support diabetes self-management. Deployed as web and mobile applications, Dietra analyzes meal photos to estimate calories and macronutrients and adapts coaching based on user behaviour and feedback. In an initial deployment with 20 adults with Type 1 or Type 2 diabetes, the system achieved 90.4% accuracy for calories,$\mathbf{9 2 \%}$for carbohydrates,$\mathbf{9 3 \%}$for protein, and$\mathbf{8 9 \%}$for fat against internal ground truth. An RL agent updates meal plans and nudges to optimize day-, week-, and month-level adherence. A dashboard displays calorie/macronutrient totals, a nutrition score, streaks, and real-time recommendations. Early results suggest that the adaptive feedback loop improves dietary awareness and supports adherence, motivating a larger clinical evaluation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".