An isotopic perspective on mass bias and matrix effects in multi-collector inductively-coupled-plasma mass spectrometry
Bibliographic record
Abstract
Lead and Tl isotope intensities and ratios were measured in radial and axial plasma profiles using multi-collector inductively-coupled-plasma mass spectrometry (MC-ICP-MS). Signal profiles for 204Pb, 206Pb, 207Pb and 208Pb display mass dependent radial and axial distributions in the plasma. In radial profiles lighter isotopes exhibit greater dispersal from the plasma axis than heavier isotopes, in agreement with ICP-MS elemental observations. However, signal maxima (Imax) for heavier Pb isotopes occur closer to the load coil than Imax for lighter Pb isotopes, the reverse of ICP-MS elemental observations. These mass dependent distributions predict that the radial dispersion and axial Imax locations for 203Tl and 205Tl isotopes should occur interspersed with the values for simultaneously measured Pb isotopes. Instead however, their values are appropriate for masses greater that 208Pb, indicating that the observed profiles are not the product of a single stage mass dependent process and that other element properties also effect the distributions. Isotope ratio (and thus mass bias) variation with sampling depth reflects both the mass dependent separation of the numerator and denominator isotope Imax along the axis of the plasma and the distance along the axis of the plasma over which the isotope signals are distributed. Addition of matrix to the plasma significantly reduces the variability of these two characteristics suggesting that more accurate correction of instrumental mass bias and more accurate normalization of sample ratios to standard values could be achieved by addition of a common matrix to samples and standards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".