Evaluation of the simultaneous use of standard additions and internal standards calibration techniques for inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
The use of internal standards combined with standard additions calibration for inductively coupled plasma mass spectrometry (ICP-MS) is discussed. Yttrium, rhodium, magnesium, cobalt, copper, nickel, silver, thallium, molybdenum and lead were analyzed in solutions of known composition with a 500 mg ml−1 Na matrix. Additionally, the Mg, Co, Y, Rh and Pb signals were used as internal standards for the other elements in the same matrix. Severe drift effects were simulated by drastically altering the liquid sample uptake rate. The signals were used to compare the results obtained using four different calibration methods: external standards, standard additions, internal standards and standard additions in combination with an internal standard. The average errors for external standards varied from 23 to 41% while the average errors for internal standards ranged from 1 to 71%. The error for standard additions was 45%. The use of standard additions with an internal standard produced an average error of 0.7–5%, suggesting that this is a powerful calibration technique.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".