Some Successful Approaches to Quantitative Mineral Analysis as Revealed by the 3<sup>rd</sup> Reynolds Cup Contest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Details of the quantitative techniques successfully applied to artificial rock mixtures distributed for the third Clay Minerals Society Reynolds Cup (RC) contest are presented. Participants each received three samples, two containing 17 minerals each and a third containing ten minerals. The true composition of the samples was unknown to all participants during the contest period. The results submitted were ranked by summing the deviations from the actual compositions (bias). The top three finishers used mainly X-ray diffraction (XRD) for identification and quantification. The winner obtained an average bias of 11.3% per sample by using an internal standard and modified single-line reference intensity ratio (RIR) method based on pure mineral standards. Full-pattern fitting by genetic algorithm was used to measure the integrated intensity of the diagnostic single-line reflections chosen for quantification. Elemental-composition optimization was used separately to constrain phase concentrations that were uncertain because the reference mineral standards were lacking or not ideal. Cation exchange capacity, oriented-sample XRD analysis, and thermogravimetric analysis were also used as supplementary techniques. The second-place finisher obtained an average bias of 13.9%, also by using an RIR method, but without an added internal standard and with intensity measured by whole-pattern fitting. The third-place finisher, who obtained an average bias of 15.3%, used the Rietveld method for quantification and identification of minor phases (using difference plots). This participant also used scanning electron microscopy (with X-ray microanalysis) to identify minor components and verify the composition of structures used in Rietveld analysis. As in the previous contests, successful quantification appears to be more dependent on analyst experience than on the analytical technique or software used.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it