Bibliographic record
Abstract
After a very successful 2007 ABRF meeting in Tampa, the FARG found itself right back at work for the 2008 meeting in Salt Lake City. At our first conference call this year, we started developing our 2008 research proposal. The resulting proposal consisted of two research studies, and both have since been approved by the Executive Board. The first study will be to conduct a survey regarding the use of SNP genotyping. This survey will be directed at users of SNP technologies through multiple sources, and will examine the who, what, and why of SNP genotyping along with how results are used from these studies. If you would be interested in participating in this survey, please watch for our survey announcements on the ABRF Web site and/or in the JBT. Second, we will present a special tutorial session for genotyping. Specifically, we will be addressing the issue of assigning genotype calls from the raw data of commonly used platforms. This will include data gathered from the Joint RG and other sources. We look forward to presenting the results of both these studies in Salt Lake City. The membership of FARG has seen some changes this year. We have added two new members: Nathan Bivens from University of Missouri-Columbia, and Glenis Wiebe, now with University of Alberta. FARG had two people leave this year: Katia Sol-Church and our EB liaison, Margaret Robinson. We thank them both for their commitment and hard work. Our new EB liaison is Michelle Detwiler, and we look forward to working with her.
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 | 0.048 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.348 | 0.277 |
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".