Additional file 1 of Variability of polygenic prediction for body mass index in Africa
Bibliographic record
Abstract
Additional file 1: Table S1. Development of the multi-ancestry (MAMA) and UKBB PRS in AWI-Gen and the Estonian Biobank. Figure S1. A. Grid of p-value thresholds (5E-08 to 1) at which PRS were computed to determine the best predictive one. B. Selected bar plots of p-value thresholds at which PRS were computed, indicating the best predictive one. Table S2. Development of the South African PRS using the AWI-Gen dataset. Table S3. Development of the West African PRS using the AWI-Gen dataset. Table S4. Clumping and LD parameters in the full AWIGen target dataset before splitting into training and validation. Table S5: Clumping and LD parameters in the AWIGen South target dataset before splitting into training and validation. Table S6. Clumping and LD parameters in the full AWIGen West target dataset before splitting into training and validation. Table S7. PRS and sex interaction models. Table S8. PRS and sex interaction models using inverse rank normalized BMI adjusted for age and principal components. Table S9. PRS and socioeconomic status interaction full model. Table S10. PRS and alcohol interaction full model. Table S11. PRS and smoking status full model. Table S12. PRS and physical activity interaction full model.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.769 | 0.111 |
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".