Bibliographic record
Abstract
International Medical Informatics Association BOARD President Hyeoun-Ae Park, Korea (2015 - 2017) President elect Christoph Lehmann, United States (2015 - 2017) Past President Lincoln de Assis Moura Jr., Brazil (2015 - 2017) Secretary Petter Hurlen, Norway (2015 - 2018) Treasurer Sabine Koch, Sweden (2012 - 2018) Vice Presidents MedInfo Kaija Saranto, Finland (2015 - 2017) Membership Michio Kimura, Japan (2012 - 2018) Services Brigitte Séroussi, France (2016 - 2019) Special Affairs Elizabeth Borycki, Canada (2016 - 2019) Working & Special Interest Groups Ying (Helen) Wu, China (2016 - 2019) CEO Elaine Huesing, Canada (2015 - 2017) IMIA Web site: www.imia.org # IMIA REGIONS APAMI: Asia Pacific Association for Medical Informatics Kyung-Hee Cho, South Korea, Vice President EFMI: European Federation for Medical Informatics Anne Moen, Norway, Vice President HELINA: Pan African Health Informatics Association Ghislain Kouematchoua Tchuitcheu, Germany/Cameroon, Vice President IMIA-LAC: Health Informatics Association for Latin America and the Caribbean Amado Espinosa, Mexico, Vice-President MEAHI - Middle East Association for Health Informatics Ramin Moghaddam, Iran, Vice President (tbc) North American Region Andre Kushniruk, Canada, Vice President
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 | 0.002 |
| 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) | 0.634 | 0.591 |
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".