RESEARCH ARTICLE Adherence to the USDA di a
Bibliographic record
Abstract
factors have also been reported in the MEC study112 Street, Edmonton, Alberta T6G 2T4, Canada Full list of author information is available at the end of the articleStroke (cerebrovascular disease) was the fourth leading cause of death in the United States in 2008, and accounted for 134,148 deaths that year [1]. Mortality from stroke differs substantially by ethnic group within the U.S. In 2008, the age-adjusted death rates from stroke among men (per 100,000 population), were 63.4 for African Americans, 39.2 for Caucasians, 24.5 for American Indians, 33.1 for Latinos, and 34.0 for Asians. The ethnic-specific rates among women were similar to those for men for Caucasians, American Indians, and American and Hispanic women (54.5 and 28.9, respect-ively) [1]. The rates of stroke fatalities are also increasing in developing countries, indicating that modifiable life-style factors, including poor diet, might be the main con-tributors to risk rather than genetic differences [2,3]. Diet is a known modifiable risk factor for stroke, and its main risk factor, hypertension [4]. The protective ef-fect of fruit and vegetables on risk of stroke has been reported in large prospective population-based studies [5-7]. Previous studies on the Multiethnic Cohort (MEC) have shown that adherence with dietary recommenda-tions and food group consumption differs substantially by ethnic group [8,9]. In addition, ethnic variations in
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.252 | 0.039 |
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".