Bridging the Gap in Garnet Diffusion Models at Low Temperatures: Recalibration Using Western Tianshan Eclogitic Breccia
Bibliographic record
Abstract
Abstract Models concerning the diffusion of divalent cations (Ca2+, Mg2+, Fe2+, and Mn2+) in garnet have been subject to extensive research and application over several decades, yet discrepancies among available models persist. Particularly the diffusion rate of Mn2+, which is the fastest in garnet, varies by more than two orders of magnitude for garnets in eclogite. In this study, we use an eclogitic breccia sample from the Western Tianshan (ultra-) high-pressure metamorphic belt for calibration. The thermobarometry indicates that the sample experienced exhumation from ~2.45 GPa, ~480°C to 1.85 GPa, ~515°C. Previous geochronological constraints estimate the exhumation duration to be a couple of million years to up to 15–20 Myr, with an average slab exhumation rate of ~3.5 mm/year. Although the estimates entail significant uncertainties, this range is still smaller than the discrepancy of Mn diffusion rate predicted among diffusion models. Thus, this natural sample provides valuable insights for calibrating the available diffusion models. Our analysis of garnet compositional profiles demonstrates that the diffusion rates (Di) at the pressure and temperature of interest are DCa:DFe:DMg:DMn = 0.2:0.4:1:2.4. By integrating garnet profiles, pressure–temperature–time information, and existing experimental data, we refine each diffusion model for Mn2+, thereby reducing the uncertainties associated with down-temperature extrapolation. Application of the newly calibrated models indicates that the oscillatory zoning of Mn2+ at the garnet rim is best described by a brief thermal excursion (~0.4 Myr, >20°C) at ~1.9 GPa. This minor thermal pulse punctuating the general exhumation path could be indicative of shear heating between nappes or momentary involvement in the mantle wedge, suggesting stacking or partial reactivation of the thrusts during exhumation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".