Bibliographic record
Abstract
Abstract: Sophie Germain was a French mathematician that lived from 1776 to 1831. Growing up, she read the works of famous mathematicians and, despite her parents’ wishes, persisted in studying mathematics. As a female, Germain faced many challenges trying to attain a formal education in mathematics. However, using an alias, she was able to correspond with some highly-regarded mathematicians at the time, such as Lagrange, Legendre and Gauss. Eventually, her true identity became known, but Germain continued to study and pursue mathematics at a very high level. Sadly, Germain was ultimately not respected as a mathematician and she struggled to overcome the societal limitations of being a female in 18th century France. In this talk, I will discuss some of her mathematical contributions and their relevance today. Germain's work has a variety of real-world applications such as in the field of cryptography. Also, her work on the subject of elasticity allowed the construction of the Eiffel Tower to be possible. Studying her work is important in understanding the historical role women have played in the field of mathematics. About the speaker: Katie Burak is a graduate student at the University of Calgary where she is pursuing her Masters of Science in Statistics study.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.039 |
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".