Additional file 3 of Genome-wide sequencing as a first-tier screening test for short tandem repeat expansions
Bibliographic record
Abstract
Additional file 3: Fig S1: exSTRa plots of EGA and simulated genomes. Fig S2: Decision tree model of the default analysis of Isaac-aligned EGA genomes on the training dataset. Fig S3: Performance metrics of the decision tree model in the default analysis of Isaac-aligned EGA genomes on the test dataset. Fig S4: Decision tree model of the default analysis of BWA-aligned EGA genomes on the training dataset. Fig S5: Performance metrics of the decision tree model in the default analysis of BWA-aligned EGA genomes on the test dataset. Fig S6: exSTRa plots of EGA genomes analyzed with 100 controls. Fig S7: Decision tree model of the modified analysis of Isaac-aligned EGA genomes on the training dataset. Fig S8: Performance metrics of decision tree model in the modified analysis of Isaac-aligned EGA test dataset. Fig S9: Allele frequency distribution of analyzed disease short tandem repeat loci in the CAUSES exomes. Fig S10: Allele frequency distribution of analyzed disease short tandem repeat loci in the CAUSES and IMAGINE genomes. Fig S11: Coverage and alignment statistics of Isaac- and BWA-aligned EGA genomes. Fig S12: Analysis of the DMPK locus by ExpansionHunter version 2 with and without off-target sites in the EGA dataset.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.631 | 0.110 |
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".