ISO 9516-1 simplified borate fusion/WDXRF analytical method for iron ore including total iron analysis: Part 2
Bibliographic record
Abstract
A very efficient analytical method using an automated fusion machine as sample preparation tool and a wavelength-dispersive X-ray fluorescence (WDXRF) spectrometer for the determination of all the elements of interest for the iron ore industry has been optimized from the ISO 9516-1 standard method. This updated method allows for the simplification of both laboratory and spectrometry processes and so, in comparison with the original International Organization for Standardization (ISO) method, becomes less restrictive in practice. This method was used to prepare a large variety of iron ores and exploration samples from all over the world. Results of the prevailing XRF application based on pure oxide standards as described in the ISO standard method are compared to the results of a unique XRF calibration application based on certified reference materials (CRMs) for iron ores and iron ores exploration materials. The universal sample preparation fusion method for iron ores and exploration samples developed during the first phase of this project was used to select and evaluate a set of CRMs. Selected throughout the world from well-recognized sources, the chosen set of CRMs allows a wide coverage for all the elements of interest of the iron ore industry, excluding tin oxide. This fusion method allows a matrix match for materials from different origins. A critical evaluation of precision and accuracy has been performed against the ISO standard method. Reference materials not included in the calibration (control samples) was also investigated for accuracy evaluation. Furthermore, comparisons will be made between the data collected from this fusion method implemented in combination with a Bruker S4 Explorer WDXRF Spectrometer, and the analytical requirements of ISO. All deviations from the prevailing standard method parameters (calibration, standards, flux, Fluxer, etc.) will also be pointed out and discussed.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".