Scientific Business Abstracts
Bibliographic record
Abstract
Dr. Xin Meng is an early-career researcher specializing in the endocrine and cellular mechanisms of human fertility and lactation. As a postdoctoral researcher in Molecular Endocrinology at the University of Oxford, Xin investigates key hormonal changes during the early postpartum period to discover endocrine biomarkers for lactation disorders such as obesity-induced low milk supply. She also characterises the biological basis of lactation with a view to developing interventions for lactation insufficiency. Prior to her postdoctoral position, Xin earned a medical degree and master’s degree focusing on infertility. She also completed a DPhil at Oxford, where she advanced research on male-factor infertility and hormone-driven reproductive processes. Insulin is a key lactation hormone as highlighted by diabetic mothers who have delayed lactation onset after childbirth. However, the role of insulin in mammary cells is unclear, and we assessed whether insulin may upregulate metabolic processes supporting milk synthesis. These studies utilised milk protein-expressing HC11 mammary cells and RNA isolated from breast milk.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.745 | 0.728 |
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".