A treasure chest full of nanogranitoids: an archive to investigate crustal melting in the Bohemian Massif
Bibliographic record
Abstract
Abstract The central European Bohemian Massif has undergone over two centuries of scientific investigation which has made it a pivotal area for the development and testing of modern geological theories. The discovery of melt inclusions in high-grade rocks, either crystallized as nanogranitoids or as glassy inclusions, prompted the re-evaluation of the area with an ‘inclusionist’ eye. Melt inclusions have been identified in a wide range of rocks, including felsic/perpotassic granulites, migmatites, eclogites and garnet clinopyroxenites, all the result of melting events albeit over a wide range of pressure/temperature conditions (800–1000°C/0.5–5 GPa). This contribution provides an overview of such inclusions and discusses the qualitative and quantitative constraints they provide for melting processes, and the nature of melts and fluids involved in these processes. In particular, data on trace-element signatures of melt inclusions trapped at mantle depths are presented and discussed. Moreover, experimental re-homogenization of nanogranitoids provided microstructural criteria allowing assessment of the conditions at which melt and host are mutually stable during melting. Overall this work aims to provide guidelines and suggestions for petrologists wishing to explore the fascinating field of melt inclusions in metamorphic terranes worldwide, based on the newest discoveries from the still-enigmatic Bohemian Massif.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".