A comparison of observed and thermodynamically predicted phase equilibria and mineral compositions in mafic granulites
Bibliographic record
Abstract
Abstract Recently published activity–composition (a–x) relations for minerals in upper amphibolite‐ and granulite facies intermediate and basic rocks have expanded our ability to interpret the petrological evolution of these important components of the lower continental crust. If such petrological modelling is to be reliable, the abundances and compositions of phases calculated at the interpreted conditions of metamorphic equilibration should resemble those in the sample under study. Here, petrological modelling was applied to six granulite facies rocks that formed in different tectonic environments and reached different peak metamorphic pressure–temperature (P–T) conditions. While phase assemblages matching those observed in each sample can generally be calculated at P–T conditions that approximate those of peak metamorphism, a consistent discrepancy was found between the calculated and observed compositions of amphibole and clinopyroxene. In amphibole, Si, Ca and A‐site K are underestimated by the model, while Al and A‐site Na are overestimated; comparatively, in clinopyroxene, Mg and Si are generally underestimated, while Fe2+ and Al are typically overestimated, compared to observed values. One consequence is a reversal in the Fe–Mg distribution coefficient (KD) between amphibole and clinopyroxene compared to observations. Some of these mismatches are attributed to the incorrect partitioning of elements between the predicted amphibole and clinopyroxene compositions; however, other discrepancies are the result of the incorrect prediction of major substitution vectors in amphibole and clinopyroxene. These compositional irregularities affect mineral modal abundance estimates and in turn the position and size (in P–T space) of mineral assemblage fields, the effect becoming progressively more marked as the modal abundance of hornblende increases; hence, this study carries implications for estimating P–T conditions of high‐temperature metabasites using these new a–x relations.
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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) | 0.000 | 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".