Mineral Systems Based on the Number of Species-Defining Chemical Elements in Minerals: Their Diversity, Complexity, Distribution, and the Mineral Evolution of the Earth’s Crust: A Review
Bibliographic record
Abstract
Abstract—The chemical diversity of minerals can be analyzed in terms of the concept of mineral systems based on the set of chemical elements that are essential for defining a mineral species. Only species-defining elements are considered to be essential. According to this approach, all minerals are classified into ten types of mineral systems with the number of essential components ranging from 1 to 10. For all known minerals, only 70 chemical elements act as essential species-defining constituents. Using this concept of mineral systems, various geological objects may be compared from the viewpoint of their mineral diversity: for example, alkali massifs (Khibiny and Lovozero in Russia; Mont Saint Hilaire in Canada), evaporite deposits (Inder in Kazakhstan and Searles Lake in the United States), fumaroles of active volcanoes (Tolbachik in Kamchatka and Vulcano in Sicily, Italy), and hydrothermal deposits (Otto Mountain in the United States and El Dragon in Bolivia). Correlations between chemical and structural complexities of the minerals were analyzed using a total of 5240 datasets on their chemical compositions and 3989 datasets on their crystal structures. The statistical analysis yields strong and positive correlations (R2 > 0.95) between chemical and structural complexities and the number of different chemical elements in a mineral. The analysis of relationships between chemical and structural complexities provides strong evidence for the overall trend of a greater structural complexity at a higher chemical complexity. Following R. Hazen, four groups of minerals representing four mineral evolution stages have been considered: (I) “Ur-minerals,” (II) minerals from chondrite meteorites, (III) Hadean minerals, and (IV) contemporary minerals. According to the obtained data, the number of species-defining elements in minerals and their average contents increase regularly and significantly from stage I to stage IV. The analyzed average chemical and structural complexities in these four groups demonstrate that both are gradually increasing in the course of mineral evolution. The increasing complexity follows an overall trend: the more complex minerals were formed in the course of geological time, without replacing the simpler ones. The observed correlations between chemical and structural complexities understood in terms of the Shannon information suggest that chemical differentiation is the major force that drives the increase of mineral complexity over the course of geological time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".