Location, Location, Location: Monazite Behaviour During UHT Metamorphism and Melt Crystallization
Bibliographic record
Abstract
ABSTRACT Monazite is a robust mineral for recording the suprasolidus evolution of migmatites and granulites. However, monazite commonly has diverse compositions and yields variable dates in a single sample; understanding the controls on monazite behaviour and composition during partial melting and melt crystallization are not always straightforward. Here, we integrate in situ monazite petrochronology from sapphirine–quartz granulites in the Arequipa Massif in Peru with an equilibrium model of monazite behaviour. Monazite from leucocratic microdomains—inferred to represent crystallized remnants of melt—is generally older and enriched in Th relative to monazite in melanosome (i.e., residual) microdomains. Enrichment and depletion of Y are unrelated to the presence or absence of garnet in these samples. An equilibrium model of monazite crystallization accounts for the decrease in Th content in monazite with decreasing date but cannot reproduce the wide range of measured Y concentrations and europium anomalies in monazite in the sapphirine–quartz granulites. We suggest that the microstructural setting of monazite is an important control on trace element composition and that whole‐rock equilibration of Eu/Eu* and Y (and the HREE) is unlikely. Monazite proximal to the principal sources and sinks of Y (garnet) and Eu (feldspar) may serve as a monitor of the behaviour of these minerals whereas monazite distal from these minerals are a monitor of evolving melt composition during crystallization. Monazite have similar dates to zircon from the same rocks, but monazite in melanocratic domains can be younger than zircon and this monazite may have been affected by late fluid ingress whereas monazite in leucosome was not. We emphasize the importance of in situ analysis of monazite and illustrate the limitations of a whole‐rock equilibrium approach to understanding accessory mineral growth and compositions in anatectic systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".