Matrix interference diagnostics for the automation of inductively coupled plasma mass spectrometry (ICP-MS)
Bibliographic record
Abstract
Matrix interferences in inductively coupled plasma mass spectrometry (ICP-MS) were examined to extend the total interference level (TIL) concept from ICP-AES. The TIL model is based on measurements with different interferents to determine a set of interference coefficients. Interference is assumed to be linear with interferent concentration, since that assumption allows determination of the model parameters with the fewest experiments. The TIL model was designed to indicate when a simple external standards calibration method is inadequate for a desired level of analytical accuracy and was also tested on a simple form of internal standards. The TIL concept was tested in both an initial calibration and a daily calibration mode on the interferents Na, K, Al, Ba and Cs and works well for situations where external standards are used, recommending an inaccurate method for only 3% of cases where 10% accuracy was desired. The TIL model also works well with the very different calibration technique of internal standards, recommending an inaccurate method for 9% of cases where 10% accuracy was desired.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".