Formation of multistage garnet grains by fragmentation and overgrowth constrained by microstructural and microchemical mapping
Bibliographic record
Abstract
Garnet is an exceptionally useful mineral for reconstructing the evolution of metamorphic rocks that have experienced multiple tectonic or thermal events. Understanding how garnet crystallizes and its mechanical behaviour, is important for establishing a petrological and temporal record of metamorphism and deformation, and to recognize multiple geologic stages within the growth history of an individual crystal. In this study, we integrate fine-scale microstructural (EBSD) and microchemical (LA-ICP-MS mapping) data obtained on a polycyclic garnet-bearing micaschist from the Alpine belt. Results suggest that fragmentation of pre-Alpine garnet porphyroblasts occurred during the late pre-Alpine exhumation and/or the onset of the Alpine burial, such that the older pre-Alpine garnet fragments were transported/redistributed during Alpine deformation and acted as new nucleation sites for Alpine garnet growth. These processes produced a bimodal garnet size distribution (macro mm-sized and micro sub-mm-sized grains). Thermodynamic modelling indicate that Alpine garnet grew during the final stage of burial (from 1.9 GPa 480 °C to 2.0 GPa 520 °C) and early exhumation (down to 1.6 GPa 540 °C) forming continuous idioblastic rims on macro- and micro-grains, and sealing fractures preserved in pre-Alpine garnet porphyroblasts. We propose that fragmentation-overgrowth processes coupled with ductile deformation in polycyclic rocks may produce a bimodal garnet size distribution and form multistage crystals resembling neoblasts. This study highlights the importance of linking microstructural (EBSD) and microchemical (LA-ICP-MS mapping) data by providing valuable information about the dominant deformation mechanisms at a given site by identifying potential links between major/trace element mobility and crystal deformation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".