Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsBarbara WiderF. U. Alakbarov is Head Scientific Officer, expert in the Oriental and Folk Medicine, Institute of Manuscripts of the Azerbaijan Academy of Sciences, 8 Istiglaliyat str., Baku, 370001, Azerbaijan.At the time of writing Liya Davydov was PharmD candidate, College of Pharmacy and Allied Health Professions, St. John's University. Currently, she is Pharmacy Practice Resident, Mount Sinai Medical Center, New York, NY.Ila Mehra Harris is Assistant Professor, Department of Pharmaceutical Care & Health Systems, College of Pharmacy, and Clinical Assistant Professor, Department of Family Practice & Community Health, Medical School, University of Minnesota, Minneapolis, MN.Colin J. Briggs is Professor of Pharmacy, Faculty of Pharmacy, University of Manitoba. Recently he completed a secondment to Health Canada, as Senior Science Advisor in the Therapeutics Products Programme with special responsibility for complementary medicines.Gemma Briggs is Research Assistant, IMPACT, The Injury Prevention Centre of Children's Hospital, 501G-715 John Buhler Research Centre, Winnipeg, MB, Canada.Mary Chavezis Professor of Pharmacy Practice, Director of Complementary Medicine Education and Research, The Center for the Advancement of Pharmacy Practice, Midwestern University, College of Pharmacy Glendale, Glendale, AZ 85308.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.000 |
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 teacher head, 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".