Anisotropy of magnetic susceptibility (AMS): magnetic petrofabrics of deformed rocks
Bibliographic record
Abstract
Abstract For 40 years magnetic anisotropy has provided successful geological interpretations of magnetic ellipsoid orientations; in contrast the interpretation of anisotropy magnitudes is far more convoluted. This is due to complexities at various levels within rocks, including different physical magnetic responses of different minerals, grain-scale magnetic anisotropy, the anisotropy of interacting ensembles, the mineralogical constitution of rocks and the processes and mechanisms that align minerals in nature. The chief factors determining the magnetic fabrics of tectonized rocks include: mineral-physics properties, crystal symmetry, mineral-abundances, tectonic symmetry and crystal orientation-distribution, strain or stress, kinematic history and certain tectono-metamorphic processes (e.g. diffusion, crystal plasticity, dynamic recrystallization, particulate flow, neomineralization). AMS ultimately provides an integrated record of some combination of these factors. Subfabrics due to distinct processes or events may be expressed in different mineral and/or grain-size fractions, and are superposed in the conventionally observed AMS. Their discrimination may be achieved by various laboratory techniques such as magnetization and torque measurements in weak and strong applied fields, anisotropy of ARM and IRM, gyroremanence, Rayleigh magnetization, chemical leaching. However, under limited circumstances, statistical approaches such as differential analysis, tensor standardization, symmetry of confidence regions for the principal axes may partly isolate different subfabric orientations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".