Wilson et al 2024 Donor Supplementary data set 1
Bibliographic record
Abstract
Identifying suitable donors for faecal microbiota transplantation (FMT) is complex, costly, and time-consuming. Here, we present the donor data set for the gut bugs trial. This is published as part of Donor screening outcomes for faecal microbiota transplantation: from recruitment to encapsulation. Brooke C. Wilsona,b,#, Ry Y. Tweedie-Cullena,#, Taygen Edwardsa, Karen S. W. Leonga, Christine Creagha, Marysia Depczynskia, Benjamin B. Alberta,c, José G. B. Derraikd,e, Justin M. O’Sullivana,b,f,g*, & Wayne S. Cutfielda,c,* a Liggins Institute, University of Auckland, Auckland, New Zealand. b The Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand.c A Better Start – National Science Challenge, University of Auckland, Auckland, New Zealand. d Department of Paediatrics: Child & Youth Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealande Environmental-Occupational Health Sciences and Non-Communicable Diseases Research Group, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailandf MRC Lifecourse Epidemiology Unit, University of Southampton, University Road, Southampton, UKg Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore #These authors contributed equally (co-primary).
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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 | 0.006 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.636 | 0.173 |
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".