Nutritional composition of commonly consumed composite dishes in Trinidad
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To calculate the nutritional composition of commonly consumed composite dishes in Trinidad in order to analyze dietary intakes obtained using a quantitative food frequency questionnaire developed specifically for the Trinidadian population. METHODS AND PROCEDURES: Multiple weighed versions of each dish were collected from 53 participants throughout Trinidad. Nutritional composition was calculated using NutriBase Clinical Nutrition Manager. MAIN OUTCOMES AND RESULTS: A total of 359 recipes were collected for 89 composite dishes: 19 vegetable, 15 starches, 21 meat/meat alternatives, eight seafood, 10 sweets, five beverages, 11 snacks/miscellaneous items. For each dish, the average nutritional composition (energy and 32 macronutrients/micronutrients) was calculated per 100 g. CONCLUSIONS: The calculated nutritional composition data of 89 commonly consumed dishes in Trinidad can now be used to assess dietary intakes and determine dietary risk factors for chronic disease.
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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".