Bibliographic record
Abstract
Introduction Grains in many rocks are not distributed randomly in space, but organised into clusters, layers and chains (Figure 6.1). Such spatial patterns can be defined in a number of ways: by the presence of grains as well as by their size, shape, orientation and mineral associations. Such non-random distributions of grains, termed patterns or packings, have been observed in metamorphic, igneous and sedimentary rocks (Rogers et al ., 1994, Jerram et al ., 2003). Despite the fact that quantitative investigation of such structures in a rock was started in 1966 (Kretz, 1966a), there have been relatively few quantitative studies since that time. Many terms have been used to describe nonuniform grain distributions: in igneous rocks grain clusters are referred to as clumps, clots or glomerocrysts, whereas in metamorphic petrology other textural terms are used. Another important textural aspect of a rock is the spatial association of minerals, or lack thereof. This may reflect nucleation, growth or mineral breakdown processes, but has been much less studied than the actual crystal positions. Patterns can be imposed on a rock during its formation by externally varying conditions or can develop spontaneously. For instance, during deposition of sediments variations in the flow regime or sediment source can produce layers; these are clearly imposed externally. Some layering in igneous rocks may result from similar sedimentary processes (Naslund & McBirney, 1996). Layering in metamorphic rocks can reflect layering of a sedimentary protolith, and deformation of dykes and enclaves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".