A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms
Bibliographic record
Abstract
This work was partly supported by the MINECO Under the TEC2015-64718-R Project, the Salvador de Madariaga Mobility Grants 2017 and the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-7103. The study was conducted in association with the National Institute for Health Research Collaborations for Leadership in Applied Health Research and Care (NIHR CLAHRC) East of England (EoE). The Project was supported by the UK Medical Research Council (Grant No. GO 400061) and European Autism Interventions — a Multicentre Study for Developing New Medications (EU-AIMS); EU-AIMS has received support from the Innovative Medicines Initiative Joint Undertaking Under Grant Agreement No. 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribution. During the period of this work, M-CL was supported by the OBrien Scholars Program in the Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health (CAMH) and The Hospital for Sick Children, Toronto, the Academic Scholar Award from the Department of Psychiatry, University of Toronto, the Slaight Family Child and Youth Mental Health Innovation Fund, CAMH Foundation, and the Ontario Brain Institute via the Province of Ontario Neurodevelopmental Disorders (POND) Network; MVL was supported by the British Academy, Jesus College Cambridge, Wellcome Trust, and an ERC Starting Grant (ERC-2017-STG; 755816); SB-C was supported by the Autism Research Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health, UK.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".