Measurement of high precision isotope ratios for mercury from coals using transient signals
Bibliographic record
Abstract
We report on high precision measurements of mercury isotopes in natural samples. The natural isotopic Hg composition in cinnabar and coal was determined using different types of ICP-MS instrumentation. The performance of 4 different multicollector (MC) ICP-MS instruments was evaluated and compared to results obtained by collision cell ICP-MS and ICP-time-of-flight-MS. Hg in cinnabar (Almaden, Spain) was continuously introduced into the ICP plasma and Hg isotope ratios were corrected for mass fractionation by measuring the 203Tl/205Tl ratio, simultaneously introduced as a dry aerosol. The average corrected ratio of 201Hg/202Hg in cinnabar using MC-ICP-MS was 0.44297 ± 0.00001 (2 SE, internal precision). This ratio differs significantly from the currently accepted IUPAC ratio for this isotope pair. Hg isotope ratios in different coal and fly ash samples were determined after the Hg in the samples was preconcentrated onto gold traps, from which the Hg was thermally desorbed into the plasma. Consequently, Hg ratios in coal were measured on transient signals. The ratios of Hg isotopes changed slightly during the evolution of the peak, suggesting a mass fractionation caused by the thermal desorption step. Hence, ratios were obtained from the integrated signal of the individual isotopes for the entire sample. The external precision between replicate samples was typically in the order of 300 to 4000 ppm (2 RSD). The external reproducibility of transient signals was similar to that from continuous signals, indicating that isotope ratio measurement on transient signals is a viable 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".