Bibliographic record
Abstract
While healthy nutrition is one of the pillars of good health, it is clear that many Americans and Canadians are falling far short when it comes to nutritional recommendations. While frank nutrient deficiency states are well-known (e.g., rickets, scurvy, pellagra), there is a growing body of evidence showing that less than optimal biochemical levels are associated with impaired cognitive function, cardiovascular disease, cancer, type-2 diabetes, poor bone health, eye disease, depression, and other conditions. Biochemical evaluation and dietary surveys show that many American/Canadians have marginal or deficient levels of key micronutrients and fatty acids. Vulnerable groups include older adults, pregnant women, strict vegetarians and vegans, those eliminating one or more food groups from their diet, those who are food insecure, those consuming a diet low in nutrient-rich foods despite adequate/excessive calorie intake, and those who have increased needs due to a health condition or chronic use of a nutrient depleting medication. Most health professionals are inadequately trained to identify those who might be at risk for deficiency, use appropriate testing to assess micronutrient levels, and make recommendations for vitamins/minerals/fatty acids to correct the deficiency. This presentation will examine the prevalence, impact and risk factors for several key micronutrient deficiencies in the American/Canadian populations from a clinician's perspective.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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; both teacher heads agree on what is shown here.
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".