Mineral systems, their types, and distribution in nature. I. Khibiny, Lovozero, and the Mont Saint-Hilaire
Bibliographic record
Abstract
In accordance with the set of species-defining chemical elements in minerals, n -component systems (where n = 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) for all mineral species (4952) known to 2014 inclusive were distinguished. Seventy chemical elements have been established to be species-defining, which are distributed by mineral systems as follows: 1 (29), 2 (62), 3 (68), 4 (61), 5 (61), 6 (55), 7 (49), 8 (38), 9 (28), and 10 (19). The number of mineral species in which certain chemical elements are species-defining has been specified. Oxygen (4041), hydrogen (2755), silicon (1448), calcium (1139), sulfur (1025), aluminum (960), iron (917), sodium (914), copper (616), phosphorous (580), arsenic (575), and magnesium (550) are the leading elements in minerals in the Earth’s crust. It has been found that the most species-defining elements are normally distributed by mineral systems. The distributions of mineral species in various systems from the Khibiny and Lovozero, Kola Peninsula, Russia; and Mont Saint-Hilaire, Quebec, Canada peralkaline plutons were compared and the characters of species-defining element distribution in these localities were compared. Si, Na, K, C, F, Ti, Ce, Zr, Nb, Sr, and Th are “excess” species-defining elements in minerals from the plutons compared to the total number of mineral species, whereas S, Cu, Pb, Cl, B, Te, Ag, Ni, and Be are “scarce” elements.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".