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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".