Depicting the crystal structure of fibrous ferrierite from British Columbia using a combined synchrotron techniques approach
Bibliographic record
Abstract
. It mainly occurs in nature with a fibrous crystal habit, and because of the existence of line/planar defects in the framework, texture and preferred orientation effects it has been difficult to obtain an exact crystallographic model based only on the results from powder diffraction data. Therefore, nano-single-crystal diffraction and tomography data have been combined in order to improve the refinement with a meaningful model. High-quality single-crystal data, providing reliable structural information, and tomography images have been used as input for a Rietveld refinement which took into account a phenomenological description of stacking disorder and the analytical description of the preferred orientation, by means of spherical harmonics for strong texture effects. This is one of the first examples of application of synchrotron nano-diffraction for the structure solution of fibrous minerals of micrometre to nanometre size. The high quality of the crystals allowed collection of single-crystal X-ray diffraction data of up to 0.6 Å resolution, leading to an unambiguous solution and precise anisotropic refinement. Nano-single-crystal diffraction and phase contrast tomography data were collected at ID11 and the high-resolution powder diffraction patterns at ID22 of the European Synchrotron Radiation Facility. This detailed crystallographic characterization provides a basis for understanding the potential of ferrierite for toxicity and carcinogenicity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".