Geometrical parameterization of the crystal chemistry of<i>P</i>63/<i>m</i>apatite. II. Precision, accuracy and numerical stability of the crystal-chemical Rietveld refinement
Bibliographic record
Abstract
A script developed for crystal-chemical Rietveld refinement ofP63/mapatite withTOPASis implemented in parallel with standard structure refinement. Least-squares standard uncertainty (s.u.) values for directly extracted crystal-chemical parameters are nearly an order of magnitude lower than those obtained indirectly by analysis of atom coordinates derived by standard Rietveld refinement. This amazing finding originates partly in the reduction of the number of refinement parameters from 21 to 17 and partly in the fact that cell data now derive from crystal-chemical parameters instead ofvice versa. Great precision and accuracy otherwise funneled into unit-cell parameters is then more distributed among mostly crystal-chemical distance parameters. The least-squares s.u. values are supported by analysis of numerous refinements of the same experimental data with added artificial intensity noise. Structural parameters from single-crystal results agree better with those extracted by crystal-chemical refinement. On the basis of singular value decomposition analyses performed using the programSVDdiagnostic[Mercieret al.(2006).J. Appl. Cryst.39, 458–465], crystal-chemical and standard Rietveld refinements are shown to have similar numerical stability. Crystal-chemical parameters extracted by direct Rietveld refinement, therefore, are more precise than, more accurate than and numerically as reliable as those derived from analysis of regular crystallographic refinement of the same data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".