Stable isotope methodology for MC-ICPMS
Bibliographic record
Abstract
Since its commercialization in 1992, the multicollector inductively coupled plasma mass spectrometry (MC-ICPMS) has quickly become a powerful research tool for the detennination of isotope amount ratios for its applications in a wide range of fields and disciplines Attributed to its inherited high sample throughput, publications of MC-ICPMS have grown exponentially over the last two decades. lt has reached a total of 12000 by March 2017, with 1400 publications in the year of 20 16 alone. This de/uge, however, has also created significant confusion and inconsistencies as to how to obtain highly accurate and precise isotope amount ratios using MC-ICPMS. Compared to the conventional therma l ionization mass spectrometry (TIMS), MC-ICPMS suffers much larger mass bias. In addition to mass-dependent fractionation (MDF), mass-independent fractionation (MIF) has also been reported in MC-ICPMS for many multi-isotopic elements. Consequently, proper choice of methodologies for correcting mass bias is of paramount importance when MC-ICPMS is used for the detennination of absolute isotope amount ratios. Current methodologies for the accurate isotope amount ratios measurements by MC-ICPMS will be reviewed and discussed in details. In particular, the latest developments in the regression mass bias correction method for the accurate isotope amount ratio measurements by MC-ICPMS and results obtained from our laboratory will be discussed in details in this lecture.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".