Comparison of mass bias correction models for the examination of isotopic composition of mercury using sector field ICP-MS
Bibliographic record
Abstract
Six mass bias correction models, viz., linear law, power law, exponential law, Russell equation, common analyte internal standardization (CAIS) and polynomial function, were evaluated for the measurement of the isotopic composition of a natural abundance mercury standard solution using a sector field ICP-MS instrument. Thallium, Os and Ir were tested as internal standards. Significant differences in resultant isotope abundances were noted when the various mass bias correction models and different internal standards were used for instrument mass bias correction. The isotopic abundances calculated from Hg ratios corrected for mass bias using a polynomial function with Os, Ir and Tl as internal standards resulted in data having the smallest relative difference (Df) from IUPAC values, which were selected as common reference points. No significant Df in results arose when the power law, exponential law and Russell equation models for mass bias correction were used in conjunction with the Tl internal standard, these exhibiting the next smallest Df values. Hg isotope abundances having the third smallest Df values arose with a linear law model. Of the three internal standards examined, use of Tl for mass bias correction provided the smallest Df values with these four models, which only require one pair of isotopes from a reference standard. The CAIS model produced the largest Df values in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".