LD matrices from the White British cohort in the UK Biobank in Zarr format
Bibliographic record
Abstract
This dataset contains the Linkage Disequilibrium (LD) matrices that were used in the analyses described in the manuscript: Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference Shadi Zabad, Simon Gravel, Yue Li McGill University LD matrices record the SNP-by-SNP correlations in a given sample of individuals from a general population. In this case, we threshold the matrices so that we only record the correlations between SNPs that are at most 3 centi Morgan apart. These matrices record the SNP correlations in a random sample of 50,000 individuals from the White British cohort in the UK Biobank dataset. There is one matrix per autosomal chromosome (chr_1, chr_2, ..., chr_22). The matrices are stored in Zarr format, a chunked on-disk array storage format that allows for multi-threaded read and write access. To access these matrices, consult the codebase of magenpy, our custom python package with special data structures for processing these LD matrices. UPDATE (03/09/2022): We updated the matrices to add the reference allele attribute (A2) and we also now have one tar archive per chromosome.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.055 |
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".