Intercomparison of PIXE and ICP‐AES Analyses of Aeolian Dust from Owens (Dry) Lake, California
Bibliographic record
Abstract
Particle‐induced X‐ray emission (PIXE) and ICP‐AES can be combined to obtain a wider range of detected elements, augment previous data and/or to verify measured concentrations of heavy and trace metals in geological samples. Intercomparison of data sets generated by multiple techniques is challenging due to the differing processes and methods of each technique. Here, we compare elemental data obtained by both techniques for aeolian dust from Owens (Dry) Lake, California. Nineteen elements were detected by PIXE, sixteen by ICP‐AES and ten elements were detected by both techniques. Statistical analyses of data set groupings illustrated which parameters differed significantly between PIXE and ICP‐AES. Relative variation (%) showed that PIXE gave higher concentrations than ICP‐AES for Al, As, Cu, Fe, K, Mn, Ni, Sr, Ti and Zn in all samples. For As, Cu, Sr and Zn, relative variation was variable between the techniques. PIXE detection limits exceeded ICP‐AES concentrations for Ba, Cd, Co, Cr, Pb and Mo. Low ICP‐AES percentage recoveries of Al, Fe, K, Mn and Ti for NIST SRM 2710 indicated incomplete dissolution during digestion (EPA Method 3050B), since elements were bound in silicate structures. Variability between the two data sets is explained by differences between analytical techniques, sample preparation methods and/or variability in sample matrices.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".