The Taxonomy of Mineral Occurrence Rarity and Endemicity
Bibliographic record
Abstract
ABSTRACT Nearly a half of known IMA-approved minerals (as of November 2021) are reported from four localities or fewer and so may be considered rare mineral species. These minerals form a continuum with more common species (e.g., rock-forming minerals), all of which constitute important constituents of Earth and contributors to its dynamics. To better understand the taxonomy of mineral rarity, evaluations have been made on the basis of k-means clustering and kernel density estimation of one-dimensional data on mineral occurrence metrics. Results from second- and third-degree polynomial regression analyses indicate the presence of a divergence between the observed number of endemic minerals discovered since 2000 and those that are likely to represent “true” endemic species. The symmetry index, calculated using the approach of Urusov for each rarity cluster, reveals a gradual decrease from ubiquitous to endemic from 0.64 to 0.47. A network analysis of element co-occurrences within each rarity cluster suggests the existence of at least three different communities having similar geochemical affinities; the latter may reflect the relative abundance of minerals their elements tend to form. The analysis of element co-occurrence matrices within each group indicates that crustal abundance is not the only factor controlling the total number of minerals each element tends to form. Other significant factors include: (1) the geochemical affinity to the principal element in the group (i.e., sulfur for chalcophile and oxygen for lithophile elements) and (2) dispersion of the principal element through geochemical processes. There is a positive correlation between the lithophile element group's abundance in the Earth's crust and the number of common minerals they tend to form, but a negative correlation with the number of rare species.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".