Generating High Mg-numbers and Chemical Diversity in Tonalite-Trondhjemite-Granodiorite (TTG) Magmas during Melting and Melt Segregation in the Continental Crust
Bibliographic record
Abstract
Major, trace, and rare earth element compositions of both tonalite–trondhjemite–granodiorite (TTG) and modern adakite-like magmas are typically used in conjunction with batch melting experiments and models to infer source rock composition, depth of melting and tectonic setting. However, batch melting does not capture the impact of melt segregation processes on magma geochemistry. We have used melting experiments in conjunction with numerical modelling to investigate the impact of melt segregation on TTG arc crust formation. Our melt segregation equilibrium (MSE) experiments are designed to reproduce the local changes in bulk composition that are predicted by the numerical model to occur as buoyant melt migrates upwards along grain boundaries and accumulates to form a magma that leaves the source region. The MSE experimental results show distinct differences in the melt and solid phase compositions and solid phase stability when compared with direct partial melting experiments. They yield a significant reduction in hornblende and plagioclase modal proportions at lower temperatures and partial melt compositions that are lower in An and have higher Mg-numbers. These results suggest that dynamic melt segregation and equilibrium processes may have a significant impact on modes, melt compositions and geochemical indicators such as Mg-numbers. Mantle wedge interaction may not be necessary to generate varying Mg-numbers in TTG and adakite magmas. Moreover, the use of batch melting models or experiments to interpret these geochemical signatures may not be appropriate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".