Geometry, scaling relationships and emplacement dynamics of a ca. 6 Ma shallow felsic sill complex, Calamita Peninsula, Elba Island, Italy
Bibliographic record
Abstract
7 Conclusions The Calamita sills exhibit similar geometric scaling to other felsic sills, once sampling bias is considered. For the Elba data unrealistically high magma viscosities and or volumetric flow rates are required to fit the viscosity dominated regime. However, the fracture-toughness dominated regime brackets the data well if field-scale fracture toughness values exceed laboratory values by one to two orders of magnitude. Such high effective fracture toughness values are feasible if there is extensive interaction and branching between growing sills and or if magma freezing at sill extremities leads to armoring of the crack tip process zone. Similar mechanical questions arise for the scaling of other felsic sills as well as mafic dykes. References Bunger, A.P. , and E. Detournay (2005), Asymptotic solution for a penny-shaped near-surface hydraulic fracture. Engineering fracture mechanics, 72(16):2468-2486. Cruden, A., and K. McCaffrey (2006), Dimensional scaling relationships of tabular igneous intrusions and their implications for a size, depth, and compositionally dependent spectrum of emplacement processes in the crust. EOS Trans. AGU, 87, Abstract V12B-06. Mazzarin, F., and Musemeci, G. (2008), Hydrofracturing-related sill and dyke emplacement at shallow crustal levels: the Eastern Elba Dyke Complex, Italy. Geol. Soc. London, Sp. Publ. 320, 121-129. 1 Dimensional Scaling of Tabular Intrusions
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".