Correction to: Genome-wide sequencing as a first-tier screening test for short tandem repeat expansions
Bibliographic record
Abstract
It was highlighted that in the original article [1] the list of the authors belonging in the IMAGINE and CAUSES Study were erroneously interchanged. The original article has been updated. Acknowledgements We would like to thank all the CAUSES and IMAGINE Study investigators. CAUSES Study investigators include Shelin Adam, Christele Du Souich, Alison Elliott, Anna Lehman, Jill Mwenifumbo, Tanya Nelson, Clara van Karnebeek, Rajan-Babu et al. Genome Medicine (2021) 13:126 Page 13 of 15 and Jan Friedman. The CAUSES Study is funded by Mining for Miracles, British Columbia Children’s Hospital Foundation, and Genome British Columbia. IMAGINE Study investigators include Patricia Birch, Madeline Couse, Colleen Guimond, Anna Lehman, Jill Mwenifumbo, Clara van Karnebeek, and Jan Friedman. We thank Compute Canada for the Research Allocation Competitions allocation, which facilitated our analysis of the IMAG INE and EGA genomes, and Julia Handra for coordinating the STR molecular testing of the clinical samples.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".