Nutri Buddy AI-Your-Powered Nutrition and Diet Companion
Bibliographic record
Abstract
Diet-related disorders such as obesity, type-2 diabetes,anaemia and micronutrient deficiencies continue tosurge worldwide, yet the majority of existing “onesize-fits-all” diet applications overlook crucialvariables—local cuisine, medical lab results, budgetconstraints, and cultural or religious food practices.Nutri-Buddy fills this gap by functioning as an AIpowered,evidence-based nutrition companion thatsynthesises nine domains of user data—vital statistics,anthropometrics, medical history, laboratory reports(via on-device Vision-OCR), dietary intake andpreferences, lifestyle factors, behavioural readiness,personal goals, and food-access context. Leveraginglarge-language-model reasoning, vector-basedretrieval, and cost-aware recipe optimisation, the appgenerates (i) a timestamped 24-hour meal plantailored to the user’s country and budget, (ii) arotating three-week menu with grocery lists in localcurrency, and (iii) an interactive chat coach for realtimequeries. Dynamic guardrails automatically flagallergens, contraindicated foods, or budget overruns,while a progress dashboard visualises BMI trends,streak badges, and updated lab markers. By unifyingpersonalised meal planning, report scanning, andbehavioural coaching into a single mobile platform,Nutri-Buddy aims to democratise clinical-gradenutrition guidance for users ranging from rural lowincomecommunities to urban professionals, ultimatelycontributing to a healthier, more informed society.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.124 | 0.068 |
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".