Powder X-Ray Diffraction Analysis of Homogeneous and Heterogeneous Sedimentary Dolostones
Bibliographic record
Abstract
Abstract The %Ca (molar Ca/(Ca + Mg) × 100) in sedimentary dolomite, which ranges from 48 %Ca to 62.5 %Ca, can be determined by atomic absorption spectrometry, electron microprobe analysis, or powder X-ray diffraction analysis. The latter procedure, which relies on the fact that the position of the d104 reflection depends linearly on the Ca content, offers an easy and relatively inexpensive method of determining %Ca in dolomite. To date, such analyses have assumed that the dolostone is homogeneous with respect to the %Ca content of its constituent dolomite crystals. Herein, the XRD technique is extended to compositionally heterogeneous dolostones. A considerable amount of important information can be derived from X-ray diffractograms of dolostones if they are analyzed with peak-fitting techniques (PF-XRD). Specifically, this technique allows (1) determination of the %Ca with an accuracy of ± 0.5 %Ca, (2) identification of heterogeneous dolostone samples formed of more than one population of dolomite crystals as defined by their %Ca content, and (3) derivation of the proportion of each population of dolomite crystals with an accuracy of ± 10%. The PF-XRD technique is a powerful analytical tool that provides valuable information on the major-element composition of low-Fe dolomite. Compared to other techniques, the PF-XRD technique is accurate, rapid, relatively inexpensive, and easy to use.
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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 | 0.002 | 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) | 0.001 | 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".