Author Correction: The white matter is a pro-differentiative niche for glioblastoma
Bibliographic record
Abstract
Authors and Affiliations Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London, WC1E 6DD, UK Lucy J. Brooks, Melanie P. Clements, Daniela Kocher, Luca Richards, Sara Castro Devesa, Leila Zakka, Megan Woodberry, Michael Ellis & Simona Parrinello MRC Laboratory for Molecular Cell Biology, University College London, Gower Street, London, WC1E 6BT, UK Jemima J. Burden Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, Queen Square, WC1N 3BG, London, UK Zane Jaunmuktane & Sebastian Brandner Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N 3BG, London, UK Zane Jaunmuktane & Sebastian Brandner MRC Centre for Regenerative Medicine and Edinburgh Cancer Research UK Cancer Centre, University of Edinburgh, 5 Little France Drive, Edinburgh, EH16 4UU, UK Gillian Morrison & Steven M. Pollard Division of Neurosurgery, Arthur and Sonia Labatt Brain Tumor Research Center, Departments of Surgery and Molecular Genetics, Hospital for Sick Children, Toronto, ON, M5G 1X8, Canada Peter B. Dirks MRC London Institute of Medical Sciences, Du Cane Road, London, W12 0NN, UK Samuel Marguerat Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK Samuel Marguerat Authors Lucy J. Brooks View author publications You can also search for this author in PubMed Google Scholar Melanie P. Clements View author publications You can also search for this author in PubMed Google Scholar Jemima J. Burden View author publications You can also search for this author in PubMed Google Scholar Daniela Kocher View author publications You can also search for this author in PubMed Google Scholar Luca Richards View author publications You can also search for this author in PubMed Google Scholar Sara Castro Devesa View author publications You can also search for this author in PubMed Google Scholar Leila Zakka View author publications You can also search for this author in PubMed Google Scholar Megan Woodberry View author publications You can also search for this author in PubMed Google Scholar Michael Ellis View author publications You can also search for this author in PubMed Google Scholar Zane Jaunmuktane View author publications You can also search for this author in PubMed Google Scholar Sebastian Brandner View author publications You can also search for this author in PubMed Google Scholar Gillian Morrison View author publications You can also search for this author in PubMed Google Scholar Steven M. Pollard View author publications You can also search for this author in PubMed Google Scholar Peter B. Dirks View author publications You can also search for this author in PubMed Google Scholar Samuel Marguerat View author publications You can also search for this author in PubMed Google Scholar Simona Parrinello View author publications You can also search for this author in PubMed Google Scholar Corresponding author Correspondence to Simona Parrinello .
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".