How to Create a Student‐Generated Database, in a Large Nutrition Class, to Illustrate the Analysis of Nutrient and Food Intakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The completion of a 3‐d food record, using commonly available nutrient analysis software, is a typical assignment for students in nutrition and food science programs. While these assignments help students evaluate their personal diets, it is insufficient to teach students about surveys of large population cohorts. This paper shows how the Test, Survey, and Pools tool in the learning management system Blackboard™ (Blackboard Inc.) was used to collect the individual food and nutrition intake data from the 3‐d food records of students in a large introductory nutrition class. This student‐generated database was then used to illustrate population level analyses. Examples of the types of analyses include (a) use of the Estimated Average Requirement cut point method to identify nutrients of concern; (b) the use of food intakes to determine the proportion of students consuming the recommended servings of foods from each food group; (c) the analysis of intakes of nutrients that are overconsumed such as salt, saturated fat, and trans fat; and (d) correlations between macronutrients (for example, as fat intake increases, carbohydrate intake decreases). The use of a database, derived from the students own food intakes, connects with student interests, and the analysis of such a database illustrates an authentic task in the nutritional sciences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it