Bibliographic record
Abstract
Research Article| January 01, 2021 Thank You Elena Sokolova Elena Sokolova Search for other works by this author on: GSW Google Scholar The Canadian Mineralogist (2021) 59 (1): 7–8. https://doi.org/10.3749/canmin.INT009 Article history received: 21 Dec 2020 first online: 01 Jul 2021 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Elena Sokolova; Thank You. The Canadian Mineralogist 2021;; 59 (1): 7–8. doi: https://doi.org/10.3749/canmin.INT009 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyThe Canadian Mineralogist Search Advanced Search I am very grateful to Yulia Uvarova and Frank Hawthorne for organising this volume, and to everyone who contributed papers to this volume. I would like to express my gratitude to people who inspired me to become a scientist and to those with whom I interacted and collaborated throughout my scientific career. First thanks go to my family. My paternal and maternal grandfathers were Professors of Biology and Chemistry, and my father Vadim Kazansky was Professor of Geology and Mineralogy, so I grew up in a family in which science was not just a job but a way of life.... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.782 | 0.773 |
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".