Bibliographic record
Abstract
In the early 1990s, when the second 5-year plan for the Human Genome Project-which requested more money than any previous research project in biology-was written, common disorders were presented as the future target of genome research. This was a clever move to ensure continued public support for this endeavor, which had been justified previously by the prospect that it would lead to the diagnosis, prevention, and therapy of severe, but mostly rare, Mendelian disorders. Today, more than 15 years later, after billions of dollars have been spent on genome-wide association studies (GWAS), very few major genetic risk factors for common diseases have been identified, and the enthusiasm for large GWAS is dwindling. At the same time, there is renewed interest for studying single gene disorders, which are now considered by some as a better clue to the understanding of common diseases. While this is probably true, Mendelian disorders are also important in their own right, since they must be far more common than generally thought. As discussed here, various efficient strategies exist for the elucidation of single gene defects, and their systematic application in combination with novel genome partitioning and massive parallel sequencing techniques, will have far-reaching implications for health care.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.110 | 0.114 |
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".