Bibliographic record
Abstract
After 12 years as Editor-in-Chief of Fetal and Maternal Medicine Review I have decided to step down, albeit I welcome the opportunity to stay on as an Associate Editor. The last 12 years have witnessed a lot of changes, not only in the field of Perinatal Medicine but also in medical publishing, with the near demise of the ‘paper-based’ journal. I have enjoyed working with Cambridge University Press who have embraced these changes and remained strong supporters of the ‘Review Journal’ concept. The quality of the papers published over this time has progressively improved and we have continued my initial vision of publishing not only clinical but scientific reviews in a range of topics related to Maternal and Fetal Medicine. I owe a huge debt of gratitude to all the Associate Editors who have contributed over the last 12 years; Karel Marsal (Lund, Sweden), Lucilla Poston (London, UK), Mark Kilby (Birmingham, UK), Tze Kin Lau (Hong Kong) and Jason Waugh (Newcastle, UK). However I would particularly like to thank John Kingdon (Toronto, Canada) and Carl Weiner (Kansas, USA) who have stayed the course and whose sage contributions have been outstanding. Most importantly I would like to acknowledge the assistance of Jean Birtles whose endless detective work and ‘polite reminders’ have made my job considerably easier. Jean is also stepping down as Editorial Assistant.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.031 |
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".