Bibliographic record
Abstract
Research Article| January 01, 2002 Mineralogy of Beryllium in Granitic Pegmatites Petr Černý Petr Černý Department of Geological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2, p_cerny@umanitoba.ca Search for other works by this author on: GSW Google Scholar Author and Article Information Petr Černý Department of Geological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2, p_cerny@umanitoba.ca Publisher: Mineralogical Society of America First Online: 14 Jul 2017 © The Mineralogical Society Of America Reviews in Mineralogy and Geochemistry (2002) 50 (1): 405–444. https://doi.org/10.2138/rmg.2002.50.10 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Petr Černý; Mineralogy of Beryllium in Granitic Pegmatites. Reviews in Mineralogy and Geochemistry 2002;; 50 (1): 405–444. doi: https://doi.org/10.2138/rmg.2002.50.10 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 SocietyReviews in Mineralogy and Geochemistry Search Advanced Search Beryllium is one of the most widespread rare elements in granitic pegmatites. These rocks have been historically the sole industrial source of this metal (e.g., Norton et al. 1958), and they still contribute a significant proportion of the global output of beryllium ores. Hand-cobbed beryl constitutes a substantial proportion of beryllium ore concentrates in Africa, Asia and South America, although non-pegmatitic, rhyolite-related bertrandite ores are virtually the single source in North America (Petkof 1975). The mineralogy of beryllium in granitic pegmatites is strongly diversified, but very “imbalanced” in terms of numbers of species per mineral class on one hand... 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".