Bibliographic record
Abstract
Folder 1. 3D modeling methods in Structural Geology represent powerful tools for a various range of applications, including (but not limited to) structural characterization, volume assessments, restoration or simply visualization of complex structural features and attitudes measurement. In this study we performed a 3D model of collected sheath fold (Alsop & Holdsworth, 2012) and related folded internal layers (here named as ‘marker’), in order to provide a detailed visualization of the geometric array and mutual relation of ductile structures. Markers are defined by metamorphic foliation resulted from minerals layering/orientation. Among those, 9 markers layers (markers 3 to 11, see Supp. Fig. 1) are clearly detectable across the sheath fold sections and slices, and were here modelled. To work within the Move environment, the model dimensions have been scaled to 1:10.000, so that 100 m in the model correspond to 1 cm. The workflow followed to develop the 3D model is summarized in Fig. 2d-h (in the main text). The first step was the mutual positioning of sections and slices in the 3D environment (Fig. 2d). Then, we picked the marker layers of sheath fold (previously interpreted by Alsop & Holdsworth, 2012) within the orthogonal sections and transversal slices (Fig. 2e). 2D picked linear features (i.e., marker layers) were 3D modelled using statistical interpolation methods (Ordinary Kriging). To simplify this procedure, the development of marker surfaces has been performed separately for the upper and the lower limbs (Fig. 2f). Individual limbs are then merged to create the 3D surfaces (Fig. 2g). Folder 2. EBSD maps with highlighted grains and vertical lines used to count subgrains for the paleo-stress calculation. Folder 3. Neutron tomography of samples taken from different domains (18a-3; 17a-5; 16a-4; 15a-8; files are available on request) and reference frame. Four specimens (15a-8; 16a-4; 17a-5; 18a-3; size ca. 15x15x15 mm) have been investigated to retrieve mineral phases 3D spatial distribution (for instrumental detailed information please refer to Garbe, et al., 2015) at ANSTO laboratory. We used the ANDOR MARANA (2048*2048) sCMOS sensor camera with the following settings: 30 µm thick Gadox scintillation screen; Pixel size: 17 mm; Step angle: 0.17°; Projection #: 1060; Exposure time: 90s; actual spatial resolution of ̴ 50 mm (Siemens star spoke target). Data have been prepared by means of Neutompy Software to correct dark and bright spots and have been processed and reconstructed by the Octopus Software for flat field normalization, flux fluctuation, and dark current correction; tilt and rotation axis correction; Fourier Back Projection Radon Transform; ring artefacts suppression in frequency and real space domains. Finally, data have been visualized and evaluated (anisotropic diffusion; unsharp masking; threshold segmentation) by means of the AVIZO Software.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.060 |
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".