Bibliographic record
Abstract
Magmatic enclaves record the history of deformation and disaggregation (i.e., fragmentation) of relatively hot, compositionally more mafic magmas injected into actively convecting silicic magma chambers through dikes. Enclave size distributions may provide crucial clues for understanding the nature of this mechanical mixing process. Accordingly, we conduct a comprehensive field study to measure enclave size distributions in six Cascade lava flows. Using results from recent fluid dynamics experiments along with thermodynamic and modeling constraints on key physical properties of the injected and host magmas (i.e., temperature, density and effective viscosity), we use the size distributions of enclaves to characterize the magmatic flow regime governing enclave formation. Scaling arguments suggest that the viscous stresses related to magma chamber flow acting against the yield strength of a crystallizing injected magma control the breakup of 1 m‐wide mafic dikes into millimeter‐ to centimeter‐scale enclaves. Our data analysis identifies a characteristic length scale of breakup that constrains the yield strength of the injected magmas in a more restrictive way than existing empirical models for yield strength based on crystal content. In all six lava flows, we show that the progressive fragmentation of the injected magma is self‐similar and characterized by a fractal dimension D f ∼ 2, which is comparable to previous studies on enclaves. We also find a small but statistically significant dependence of D f on the effective viscosity ratio between host and enclave magmas, such that large variations in effective viscosity enhance breakup. This work demonstrates that field observations of enclave size distributions can reliably constrain the rheological and flow conditions in which enclaves form.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".