Additional file 1 of Multidimensional associations between nutrient intake and healthy ageing in humans
Bibliographic record
Abstract
Additional file 1. Contains the following. Text S1. Model Descriptions. Text S2. Analysis of Inclusive Dataset with Imputation of Missing Income Data. Text S3. Analysis of Exclusive Dataset. Text S4. Estimation of Relative Intake. Text S5. Dietary Macronutrient Composition and Micronutrient Intake. Table S1. Variables and Summary Statistics. Tables S2 though S5, S8 & S9. GAM outputs. Table S6. Principal Component Analysis. Table S7. Models with Significant Effects of Micronutrients. Table S10 & S11. Model AICs. Figure S1. & S3. Effects of Relative Macronutrient Intake. Figure S2 & S4. Effects of Dietary Micronutrients. Figure S5. Effects of Diet Macronutrient Composition on Micronutrient intake. Figure S6. Standard Errors for Effects of Macronutrients.
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.
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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.839 | 0.154 |
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".