<i>In Situ</i> Diffraction Studies: Thermal Decomposition of a Natural Plumbojarosite and the Development of Rietveld-Based Data Analysis Techniques
Bibliographic record
Abstract
A study of the thermal decomposition sequence of a sample of natural arsenian plumbojarosite has been undertaken using in situ X-ray diffraction. The sample was heated to 900°C using an Anton-Paar heating stage fitted to an INEL CPS120 diffractometer. The data were analysed using a whole-pattern, Rietveld based approach for the extraction of quantitative phase abundances. The instrument configuration used required the development and application of algorithms to correct for aberrations in the (i) peak intensities due to differing path lengths of incident and diffracted beams in the sample and (ii) peak positions due to sample displacement. Details of the structural models used were refined at selected steps in the pattern and then fixed for subsequent analysis. The data sequence consists of some 110 individual data sets which were analysed sequentially with the output of each run forming the input for analysis of the next data set. The results of the analysis show a complex breakdown and recrystallisation sequence including the formation of a major amount of amorphous material after initial breakdown of the plumbojarosite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".