Intuitive Data-Driven Visualization of Food Relatedness via t-Distributed Stochastic Neighbor Embedding
Bibliographic record
Abstract
The relationship between diet and health is important, yet difficultto study in practice. Dietary pattern analysis is one method forinvestigating this link; having more variety in diet tends to be bene-ficial and a score can be generated based on a heuristic approachto food intake habits. We aim to enhance the intuition behindthese food scores by creating an intuitive data-driven visualizationof food relatedness by leveraging t-distributed stochastic neighborembedding (t-SNE). More specifically, by performing t-SNE anal-ysis in a controlled manner to project the high-dimensional nutri-tional information of food items into a lower dimensional food sim-ilarity space, the natural clustering of foods based on the underly-ing nutritional composition becomes visually observable. The effi-cacy of this data-driven approach for visualizing food relatednesswas investigated on a total of 8549 food item entries in the USDAfood composition database, with the results showing considerablepromise as a tool for gaining important nutritional insights. This isthe first step toward providing a novel method to enhance dietarypattern analysis with additional context and insight into food intakehabits based on the inherent nutritional content of the foods con-sumed.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".