Bibliographic record
Abstract
Research Article| April 01, 2013 New Mineral Names* Dmitriy Belakovskiy; Dmitriy Belakovskiy 1Fersman Mineralogical Museum, Russian Academy of Sciences, Moscow 119071, Russia Search for other works by this author on: GSW Google Scholar Dorian G.W. Smith; Dorian G.W. Smith 2Department of Earth and Atmospheric Sciences, 1-26 Earth Sciences Building, University of Alberta, Edmonton, Alberta T6G 2E3, Canada Search for other works by this author on: GSW Google Scholar Mark Welch Mark Welch 3Department of Mineralogy, The Natural History Museum, Cromwell Road, London SW7 5BD, U.K. Search for other works by this author on: GSW Google Scholar American Mineralogist (2013) 98 (4): 811–815. https://doi.org/10.2138/am.2013.608 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Dmitriy Belakovskiy, Dorian G.W. Smith, Mark Welch; New Mineral Names. American Mineralogist 2013;; 98 (4): 811–815. doi: https://doi.org/10.2138/am.2013.608 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentBy SocietyAmerican Mineralogist Search Advanced Search This New Mineral Names has entries about aklimaite, davinciite, menzerite, schüllerite, vladimirivanovite, and volaschioite from journals around the world. A.E. Zadov, I.V. Pekov, N.V. Zubkova, V.M. Gazeev, N.V. Chukanov, V.O. Yapaskurt, P.M. Kartashov, E.V. Galuskin, I.O. Galuskina, N.N. Pertzev, A.G. Gurbanov, and D.Yu. Pushcharovsky (2012) Aklimaite, Ca4[Si2O5(OH)2](OH)4·5H2O, a new natural hydrosilicate from Lakargi area (the North Caucazus, Russia). Zap. Ross. Mineral. Obshch., 141(2), 21–31 (in Russian, English abstract). N.V. Zubkova, I.V. Pekov, D.Yu Pushcharovsky, A.E. Zadov, and N.V. Chukanov (2012) The crystal structure of aklimaite, Ca4[Si... You do not currently have access to this article.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.580 | 0.408 |
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".