Bibliographic record
Abstract
President Dr. Christoph Lehmann, United States (2017–2019) President elect Dr. Sabine Koch, Sweden (2017–201 9) Past President Dr. Hyeoun-Ae Park, South Korea (2017–2019) Secretary Dr. Petter Hurlen, Norway (2015–2021) Treasurer Johanna Westbrook, Australia (2017–2020) Vice Presidents MedInfo Dr. Patrick Weber, Switzerland (2017–2019) Membership Dr. Daniel Luna, Argentina (2018–2021) Services Dr. Brigitte Seroussi, France (2016–2019) Special Affairs Dr. Elizabeth Borycki, Canada (2016–2019) Working & Special Interest Groups Dr. Ying (Helen) Wu, China (2016–2019) CEO Elaine Huesing, Canada IMIA Web site: www.imia.org Regional Vice Presidents to IMIA APAMI: Asia Pacific Association for Medical Informatics Dr. Vajira Dissanayake, Sri Lanka EFMI: European Federation for Medical Informatics Dr. Christian Lovis, Switzerland HELINA: Pan African Health Informatics Association Dr. Ghislain Kouematchoua Tchuitcheu, Germany/Cameroon IMIA-LAC: Health Informatics Association for Latin America and the Caribbean Marcelo Lucio da Silva, Brazil MENAHIA: Middle East and North African Health Informatics Association Dr. Riyad Al Shammari, Saudi Arabia North American Region Andre Kushniruk, Canada IMIA Liaison Officers, ex officio WHO Liaison Officer Dr. Antoine Geissbuhler, Switzerland IFIP Liaison Officer Dr. Hiroshi Takeda, Japan ISO Liaison Officer Dr. Michio Kimura, Japan
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.713 | 0.682 |
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".