Hidden Figures: Eminent Women of Applied Mathematics
Bibliographic record
Abstract
Abstract: In this talk, I will take the audience on a tour of societies of recognition, such as the US National Academy of Sciences and the Fellows of the Society for Industrial and Applied Mathematics. I will select some of the eminent female applied mathematicians from these lists, delve into their personal stories and careers, and celebrate the contributions that they made to the field. About the speaker: Gerda de Vries is Professor in the Department of Mathematical & Statistical Sciences and Associate Dean Undergraduate for the Faculty of Science at the University of Alberta. She is passionate about teaching at all levels and has won many teaching awards, including the Rutherford Award for Excellence in Undergraduate Teaching at the University of Alberta (2006) and the Canadian Mathematical Society Excellence in Teaching Award (2014). Gerda is an advocate of interdisciplinary education, with an eye to making connections between mathematics and its applications to all sciences. She is a sought-after public speaker, presenting on the importance (and joy) of mathematics. Gerdaâ s research lies at the interface of mathematics and biology, and encompasses applications to cell biology, neurophysiology, the formation of animal groups, and industrial-scale composting.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.115 | 0.036 |
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".