Cell scientist to watch – Pleasantine Mill
Bibliographic record
Abstract
ABSTRACT Pleasantine Mill graduated in microbiology and immunology from McGill University, Montréal, Canada, and completed her PhD in medical and molecular genetics at the University of Toronto, Canada. There, under the supervision of Chi-chung Hui, she studied Hedgehog signalling pathways in skin development and tumorigenesis. For her postdoctoral research, Pleasantine moved to Ian Jackson's laboratory at the MRC Human Genetics Unit, Edinburgh, UK, with a Natural Sciences and Engineering Research Council of Canada (NSERC) fellowship, followed by a Caledonian Research fellowship. Initially focusing on neural crest development, she identified and studied several mouse mutants that displayed defects in ciliogenesis and cilia structure that went on to be implicated in human disease. Pleasantine established her own research program at the MRC in 2014. Her group investigates the genetic programme for cilia structure and function and the links to human ciliopathies. She is the recipient of the 2019 Women in Cell Biology Early Career Award Medal from the British Society for Cell Biology (BSCB).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.137 | 0.054 |
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".