Portable X-ray fluorescence in the assessment of rare earth element-enriched sedimentary phosphate deposits
Bibliographic record
Abstract
Phosphate deposits are the main source of raw materials used by the phosphate fertilizer industry and, in recent years, they have also been considered as potential sources of rare earth elements (REEs) and fluorine (F). Based on 160 portable XRF (pXRF) measurements taken on 32 pulps of naturally occurring phosphate rocks from the Fernie Formation (British Columbia, Canada) with a representative range of P and REE concentrations, the pXRF analyser was able to provide data with acceptable precision, accuracy, and excellent coefficients of determination (r 2 ≥ 0.85) relative to the laboratory data for Ba, Mo, Y, Sr, U, Rb, Zn, Fe, Ca, P, Si and S. Results with coefficients of determination of 0.5 ≤ r 2 < 0.85 between the pXRF and laboratory-derived datasets were achieved for Nd, Ce, La, Zr, W, and Al. From an exploration geologist’s point of view, P and Fe were measured accurately enough by pXRF using the factory-set calibration. Portable XRF analysis for Nd, Pr, Ce, La, Ba, Mo, Zr, Y, Sr, U, Rb, Zn, Ca, K, Al, Si and S had to be recalibrated relative to laboratory results. Using factory pXRF settings, light REEs (La, Ce, Pr, Nd) were subject to systematic overestimation relative to laboratory results whereas Y was subject to systematic underestimation. This study presents the first stage of an orientation survey (pulp analyses) for sedimentary phosphate occurrences containing REEs in southeastern British Columbia. If sample preparation (to pulps) and field recalibration are carried out, pXRF can identify zones of phosphate rocks enriched in REEs, and delimit zones with unacceptable levels of deleterious elements, such as uranium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".