Clinopyroxene/Melt Trace Element Partitioning in Sodic Alkaline Magmas
Bibliographic record
Abstract
Abstract Clinopyroxene is a key fractionating phase in alkaline magmatic systems, but its impact on metal enrichment processes, and the formation of REE + HFSE mineralisation in particular, is not well understood. To constrain the control of clinopyroxene on REE + HFSE behaviour in sodic (per)alkaline magmas, a series of internally heated pressure vessel experiments was performed to determine clinopyroxene–melt element partitioning systematics. Synthetic tephriphonolite to phonolite compositions were run H2O-saturated at 200 MPa, 650–825°C with oxygen fugacity buffered to log f O2 ≈ ΔFMQ + 1 or log f O2 ≈ ΔFMQ +5. Clinopyroxene–glass pairs from basanitic to phonolitic fall deposits from Tenerife, Canary Islands, were also measured to complement our experimentally-derived data set. The REE partition coefficients are 0·3–53, typically 2–6, with minima for high-aegirine clinopyroxene. Diopside-rich clinopyroxene (Aeg5–25) prefer the MREE and have high REE partition coefficients (DEu up to 53, DSm up to 47). As clinopyroxene becomes more Na- and less Ca-rich (Aeg25–50), REE incorporation becomes less favourable, and both the VIM1 and VIIIM2 sites expand (to 0·79 Å and 1·12 Å), increasing DLREE/DMREE. Above Aeg50 both M sites shrink slightly and HREE (VIri ≤ 0·9 Å ≈ Y) partition strongly onto the VIM1 site, consistent with a reduced charge penalty for REE3+ ↔ Fe3+ substitution. Our data, complemented with an extensive literature database, constrain an empirical model that predicts trace element partition coefficients between clinopyroxene and silicate melt using only mineral major element compositions, temperature and pressure as input. The model is calibrated for use over a wide compositional range and can be used to interrogate clinopyroxene from a variety of natural systems to determine the trace element concentrations in their source melts, or to forward model the trace element evolution of tholeiitic mafic to evolved peralkaline magmatic systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".