General Equation for Multiple Spiking Isotope Dilution Mass Spectrometry
Bibliographic record
Abstract
Isotope dilution is a well-known primary ratio method of quantitative analysis that yields good-quality metrological results. Many equations have been proposed to calculate the amount of substance from the isotope ratio measurements, and these have been used successfully for more than a half-century. Decades ago, isotope dilution equations were extended to correct for analyte formation during analysis, which is especially apparent in the analysis of methylmercury or chromium(VI). Considering only methods for the determination of these two analytes, many variables that are involved must be considered (for example, the extent of analyte formation, the number of isotopes monitored for each analyte, the number of substances, or the nature of mass spectra (elemental versus molecular)). To date, no master equation that can adequately address all of these aspects of the problem has been proposed. In this manuscript, we propose a general equation for isotope dilution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".