THE USE OF CHEMICAL-ELEMENT ADJECTIVAL MODIFIERS IN MINERAL NOMENCLATURE
Bibliographic record
Abstract
Chemical-element adjectival modifi ers are not part of the name of a mineral species. Schaller-type adjectival modifi ers, which have the endings-oan or-ian, formerly recommended by the CNMMN of the IMA, in many cases give erroneous information about the valence of an ion, and are therefore inappropriate. Instead of such modifi ers, the CNMMN has now approved a proposal that chemical-element adjectival modifi ers employing the chemical-element symbol or the name of the chemical element together with a descriptive term should be used. The valence (nominal numerical charge plus sign) or the numerical oxidation state may be added, if required. Authors should therefore feel free to use chemical-element adjectival modifi ers that are chemically correct and that meet their particular requirements. For example, chemical-element adjectival modifi ers such as “Mg-rich”, “Mg–Fe-rich”, “Fe2+-poor”, “iron(2+)-enriched”, “iron(II)-bearing”, “alkali-defi cient”, “sodium-exchanged”, “Cr-doped”, or “H2O-saturated” may be used. Synthetic or hypothetical analogues of mineral species or natural analogues of mineral species unapproved by the CNMMN could be written with a chemical-element(s) suffi x. The synthetic product “topaz-OH ” is the OH-dominant analogue of topaz, Al2SiO4F2. The use of quotation marks around “topaz-OH ” is essential to show that the name is not approved by the CNMMN and to avoid confusion with names of real mineral species, such as chabazite-Ba. A chemical-element symbol should not be used as a prefi x to a name of a mineral species. However, if used in a diagram, table, or running heading owing to space limitations, then a correct version must be used in the text together with the short version in quotation marks to show that the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".