Parameterizations of calcic clinopyroxene—Melt trace element partition coefficients
Bibliographic record
Abstract
Semiempirical parameterizations of the values and trends of variation of Nernst trace element partition coefficient data for Ca‐rich clinopyroxene ( cpx/liquid D) are provided, applicable mainly to common terrestrial magmatic suites. cpx/liquid D data for most trace elements show significant variability which cannot be neglected when modeling melting and crystallization. The influence of pressure on cpx/liquid D is strong for many elements, particularly Na and Sr, which increase as pressure rises, and most high‐field strength elements, which decrease with increasing pressure. Most cpx/liquid D values increase as temperature decreases, as wt % melt MgO, MgO# (MgO/MgO+FeO total ), CaO, and FeO contents drop, as cpx molar Mg# (Mg/Mg+Fe total ) decreases, and as wt % melt SiO 2 and Na 2 O+K 2 O increase. No clear trends are seen for variations of cpx/liquid D against melt H 2 O. For mafic melts, many elements show well‐defined trends of cpx/liquid D increase as the clinopyroxene tetrahedral Al content (cpx Al iv ) increases. Many cpx/liquid D are well correlated against cpx/liquid D Ti , and many “near‐neighbor” elements show good cpx/liquid D intercorrelations (e.g., Zr‐Hf, U‐Th, Nb‐Ta, La‐Ce, Yb‐Lu). Cpx/liquid D profiles calculated from these parameterizations can constrain changes of D values during melting or crystallization. Cpx/liquid D for the rare earth elements were fit to the lattice strain model to derive fits that can reproduce the cpx/liquid D REE profile shapes (REE = rare earth elements). These fits indicate that cpx/liquid D REE for melts more evolved than picritic basalts cannot be modeled assuming that all REE are in octahedral coordination in a single M2 site, but also require sixfold partitioning into an M1 site for Lu‐Yb‐Tm‐Er.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".