Influence of the number of calibration points on the quality of results in inductively coupled plasma mass spectrometry
Bibliographic record
Abstract
Quantification methods based on multiple-point calibration by weighted linear regression, and double-point calibration (measurement of the blank and one standard) were investigated under multielement routine conditions for trace analysis. Proper quantification by both calibration methods, in terms of bias and uncertainty, was achieved when compared to reference materials. The expanded uncertainty of the results was not influenced by the number of calibration standards, since a coverage factor of 2 (95% confidence level) was adopted both for double and multiple-point calibration. The use of this factor in multiple-point calibration instead of Student's t, which depends on the number of calibration points, is justified because the calculated uncertainty related to the calibration is a good estimation of the calibration standard uncertainty, due to the highly linear behaviour of the ICP-MS technique. Relative expanded uncertainties ranged from 10–15% for concentrations around the LOQ to 3–5% for concentrations higher than 100 times the LOQ.
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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 | 0.034 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".