Genetic Analysis Workshop 16: Strategies for genome-wide association study analyses
Bibliographic record
Abstract
This supplement to BMC Proceedings contains the proceedings of Genetic Analysis Workshop (GAW) 16, which was held September 17-20, 2008, in St. Louis, Missouri, USA. Initiated in 1982, the GAWs are now held in even-numbered years with the purpose of evaluating strategies for detecting genetic effects of complex diseases, thought to be the result of the joint effects of environmental and genetic factors. Each GAW meeting begins with the distribution of datasets that those who attend the Workshop use for the purpose of developing and/or evaluating statistical methods. These datasets are jointly chosen for the next Workshop through a discussion of those attending the meeting and the GAW Advisory Committee. At most Workshops, GAW has included a set of simulated datasets, so that researchers can examine the behavior of statistical methods when knowing the answer. A primary goal of the Workshops is to focus discussion on specific topics of interest and areas of methodological concern. The datasets are generally available to any researcher who requests them. Each person who desires to attend the Workshops must participate in the evaluation of at least one of the distributed datasets, investigating novel approaches or comparing emerging and existing methods. Participants also include those who have provided the data or participate in the Workshop organization. More information about GAW, including details of upcoming Workshops, may be found at http://www.gaworkshop.org .
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 | 0.097 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.036 |
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".