The Third International Genomic Medicine Conference (3rd IGMC, 2015): overall activities and outcome highlights
Bibliographic record
Abstract
The Third International Genomic Medicine Conference (3 rd IGMC) was organised by the Centre of Excellence in Genomic Medicine Research (CEGMR) at the King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia (KSA). This conference is a continuation of a series of meetings, which began with the first International Genomic Medicine Conference (1 st IGMC, 2011) followed by the second International Genomic Medicine Conference (2 nd IGMC, 2013). The 3 rd IGMC meeting presented as a timely opportunity to bring scientists from across the world to gather, discuss, and exchange recent advances in the field of genomics and genetics in general as well as practical information on using these new technologies in different basic and clinical applications. The meeting undoubtedly inspired young male and female Saudi researchers, who attended the conference in large numbers, as evidenced by the oversubscribed oral and poster presentations. The conference also witnessed the launch of the first content for npj Genomic Medicine , a high quality new journal was established in partnership by CEGMR with Springer Nature and published as part of the Nature Partner Journal series. Here, we present a brief summary report of the 2-day meeting including highlights from the oral presentations, poster presentations, workshops, poster prize-winners and comments from the distinguished scientists.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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".