Coupling forward modelling of garnet growth with monazite geochronology: an application to the Rappold Complex (Austroalpine crystalline basement)
Bibliographic record
Abstract
Abstract Results from forward modelling of garnet growth and U–Th–Pb chemical dating suggest three periods of metamorphism that affected metapelitic rocks of the Rappold Complex (Eastern European Alps). Garnet first grew during Barrovian‐type metamorphism, possibly during the Carboniferous Variscan orogeny. The second period of metamorphism produced monazite and resulted in minor garnet growth in some samples. Variable garnet growth was controlled by changes to the effective bulk rock composition resulting from resorption of older garnet porphyroblasts. Monazite crystals have variable morphology, textures and composition, but all yield Permian ages (267 ± 12 to 274 ± 17 Ma). In samples in which there was Permian garnet growth, monazite forms isolated and randomly distributed grains. In other samples, monazite formed pseudomorphous clusters after allanite. This difference is attributed to higher transport rates of monazite‐forming elements in samples which underwent dehydration reactions during renewed garnet growth. The third and final period of garnet growth took place during Eo‐Alpine (Cretaceous) metamorphism. Garnet of this age displays a wart‐like texture. This may reflect transport‐limited growth, possibly as a result of repeated dehydration during polyphase metamorphism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".