Two stars on chemistry horizon (of V. E. Bogdanovskaya and V. N. Ipatiev 150th anniversary)
Bibliographic record
Abstract
Until the first quarter of 20th century women could not obtain higher education in Russia. Lev \nGumilev commented that only most passionate of them could do it. Their quantity was not even \ndozens; only few names are known. Nevertheless, some chemists we can reveal among them, \nnamely Maria Bakulina, Margaret von Wrangel, Vera Balandina, Vera Bogdanovskaya, Anna \nVolkova, Vera Glebova, Julia Lermontova, and Lina Shtern. This paper is devoted to one of \nthem, namely Vera Evstafevna Bogdanovskaya-Popova. She was not only experimental chemist, but also the author of one of the first original Russian school textbooks in chemistry. In the history \nof chemistry in our country we can find another amazing person who has not received special \nchemical education, i. e., did not obtain the University degree in this specialty. Nevertheless, his \nworks in organic chemistry were so brilliant that he has received international recognition, as well \nas it was confirmed by his Academician degree. His name is Vladimir Nikolayevich Ipatiev. Both \nof these scientists will celebrate soon (the year 2017) the 150th anniversaries of their birthdays. \nRefs 22. Figs 9.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 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".