Solubility of manganotantalite and manganocolumbite in pegmatitic melts
Bibliographic record
Abstract
Solubility experiments of MnNb 2 O 6 and MnTa 2 O 6 were conducted in two nominally dry to watersaturated pegmatitic melts with different amounts of Li, F, P, and B at 700 to 1000 °C and 200 MPa to determine the maximum concentrations of Nb and Ta in pegmatitic melts. The Li 2 O, F, B 2 O 3 , and P 2 O 5 contents in the melts were 1.16, 2.99, 1.78, and 1.55 wt% for melt composition PEG1 and 1.68, 5.46, 2.75, and 2.75 wt% for melt composition PEG2 and the resulting Al/(Na+K+Li) ratio for both melts is 0.92. The experimental data show that the solubility product of manganocolumbite increases by a factor of three upon increasing the water concentration from 0 to 4 wt%. Considering that pegmatitic melts at pressures above 50 to 100 MPa are hydrous (>4 wt% H 2 O), the increase in solubility by this magnitude, over the stated range of water concentration, is not significant for pegmatites. The data also point out that the solubility of MnNb 2 O 6 and MnTa 2 O 6 is strongly temperature dependent, increasing by a factor of 50 for manganocolumbite and 15-20 for manganotantalite from 700 to 1000 °C under water-saturated conditions. The solubility also increases with increasing content of fluxing elements like Li, F, B, and P. In the pegmatite melt containing the highest amount of fluxing elements, the maximum concentrations of Ta and Nb are higher by nearly one order of magnitude when compared to a subaluminous rhyolitic melt.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".