Interpreting Europium Anomalies in Zircon: The Importance of Source Redox
Bibliographic record
Abstract
Abstract The sources of granitoids are variably oxidized due to the diversity of environments in which they form. This environmental and consequent chemical variability leads to differences in mineral assemblages, proportions, and compositions of ferric and ferrous iron‐bearing phases in these sources and in the resultant granitoids. Accessory minerals that grow or recrystallize during high‐temperature metamorphism and/or melt crystallization can record such redox variations through the incorporation of redox‐sensitive trace elements, notably europium. Here, we use petrological modeling to explore how variations in ferric/ferrous iron ratios of a metapelite and a hydrated (meta)basite influence the speciation of Europium (Eu 3+ vs. Eu 2+ ) in the melt and resultant Europium anomalies in zircon using phase equilibrium modeling and mass balance. Europium anomalies in zircon sourced from metapelites are generally insensitive to the proportions of ferric to ferrous iron, except at very reducing conditions. Europium anomalies in zircon sourced from metabasites are influenced by the proportion of ferric iron in the source, and more so in the absence of residual garnet. The amounts of plagioclase—which is commonly linked to pressure—play a relatively minor role in the Europium anomalies of zircon in the metabasite. Hence, Europium anomalies in zircon may not be an appropriate tool on their own to unravel past tectonic processes, including the thickness of Earth's continental crust.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".