Reducing Sodium Across the Board
Bibliographic record
Abstract
Excess sodium intake can lead to increased blood pressure. Restaurant foods contribute nearly a quarter of the sodium consumed in the American diet. The objective of the pilot project was to develop and implement in collaboration with independent restaurants a tool, the Restaurant Assessment Tool and Evaluation (RATE), to assess efforts to reduce sodium in independent restaurants and measure changes over time in food preparation categories, including menu, cooking techniques, and products. Twelve independent restaurants in Schenectady County, New York, voluntarily participated. From initial assessment to a 6-month follow-up assessment using the RATE, 11 restaurants showed improvement in the cooking category, 9 showed improvement in the menu category, and 7 showed improvement in the product category. Menu analysis conducted by the Schenectady County Health Department staff suggested that reported sodium-reduction strategies might have affected approximately 25% of the restaurant menu items. The findings from this project suggest that a facilitated assessment, such as the RATE, can provide a useful platform for independent restaurant owners and public health practitioners to discuss and encourage sodium reduction. The RATE also provides opportunities to build and strengthen relationships between public health care practitioners and independent restaurant owners, which may help sustain the positive changes made.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".