Genomic variation in a global village: Report of the 10th annual Human Genome Variation Meeting 2008
Bibliographic record
Abstract
The Centre for Applied Genomics of the Hospital for Sick Children and the University of Toronto hosted the 10th Human Genome Variation (HGV) Meeting in Toronto, Canada, in October 2008, welcoming about 240 registrants from 34 countries. During the 3 days of plenary workshops, keynote address, and poster sessions, a strong cross-disciplinary trend was evident, integrating expertise from technology and computation, through biology and medicine, to ethics and law. Single nucleotide polymorphisms (SNPs) as well as the larger copy number variants (CNVs) are recognized by ever-improving array and next-generation sequencing technologies, and the data are being incorporated into studies that are increasingly genome-wide as well as global in scope. A greater challenge is to convert data to information, through databases, and to use the information for greater understanding of human variation. In the wake of publications of the first individual genome sequences, an inaugural public forum provided the opportunity to debate whether we are ready for personalized medicine through direct-to-consumer testing. The HGV meetings foster collaboration, and fruits of the interactions from 2008 are anticipated for the 11th annual meeting in September 2009.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".