Bibliographic record
Abstract
TACKLING THE GENETIC COMPLEXity of some neurological conditions is no easy task, but researchers have begun to unravel the heritable components of autism spectrum disorders (ASDs) through studies of affected families. By analyzing the DNA of 1600 families with at least two affected individuals, the Autism Genome Project Consortium, a group of more than 120 collaborators from 50 centers in North America and Europe, performed the largest genome scan to date for ASDs, finding clues to autism susceptibility (Autism Genome Project Consortium. Nat Genet. doi:10.1038/ ng1985 [published online February 18, 2007]). Throughout the 5-year study, funded primarily by the nonprofit group Autism Speaks and the National Institutes of Health, the collaborators shared DNA samples, data, and expertise (HuLince D et al. Am J Pharmacogenomics. 2005;5:233-246). “The plan came together around the idea that we needed to increase the sample size of affected families and to have a standard set of markers genotyped with a single technology,” said coprincipal investigator Stephen Scherer, PhD, of the Hospital for Sick Children and the University of Toronto, in Ontario.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".