Practical Assessment of Quartz Crystallographic Preferred Orientation Strength
Bibliographic record
Abstract
The ordering strength of crystallographic preferred orientation data can be assessed in different ways, however, the appropriateness of the methodology used to do so can depend on the geometry of the distributions. Eigenvector-based ordering evaluation methods, for example, may not be appropriate for data that comprise multiple, variably oriented distributions such as those commonly found in quartz c-axis crystallographic preferred orientations. Examination of artificial data that represent a variety of different potential c-axis distributions shows a significant correlation between the relative orientations of those distributions (i.e., the opening angle in a cross-girdled quartz c-axis pole figure) and the strengths calculated using eigen-vector based evaluation methods; larger pole figure opening angles correlate with decreasing distribution ordering strength. The same correlation does not exist when strength is evaluated using the l2 - norm of the estimated probability density function (JPF) of the same data. The direct correlations between pole figure c-axis opening angles and ordering strength noted in the artificial distributions are also demonstrated in the evaluation of real-world data, though significant complications related to heterogeneous nature, and/or deformation, of the natural specimens can partially obfuscate the relationship. Regardless, given the potential effect of geometry on eigenvector-based evaluation methods we recommend that the ordering strength of pole figure data be evaluated using JPF.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".