Direct Determination of Lead Isotopes (<sup>206</sup>Pb, <sup>207</sup>Pb, <sup>208</sup>Pb) in Arctic Ice Samples at Picogram per Gram Levels Using Inductively Coupled Plasma-Sector Field MS Coupled with a High-Efficiency Sample Introduction System
Bibliographic record
Abstract
Adopting strict cleanroom procedures, ice samples from the Canadian High Arctic have been analyzed for Pb concentrations and Pb isotopes (206Pb, 207Pb, 208Pb) using ICP-SMS. The detection limit for Pb (0.06 pg g(-1)) was approximately 2 orders of magnitude lower than the lowest concentration of Pb in the ice samples (range, 4.3-1660 pg g(-1); median, 45 pg g(-1)). Acidification of ice samples with high-purity HNO3 for stabilization purposes contributed only 0.004 pg of Pb g(-1), which is an insignificant source of Pb. Using a new sample introduction system consisting of a heated (140 degrees C) minicyclonic spray chamber and a Peltier cooled condenser (2 degrees C) and by replacing the conventional sample cone with a high-performance cone, signal intensities for Pb were increased by approximately 1 order of magnitude. Thus, it was possible not only to measure Pb isotope ratios directly using ICP-SMS but also to achieve reasonable precision (approximately 0.2%) at low picogram per gram concentrations of total Pb. This precision is comparable to that achievable by thermal ionization mass spectrometry at such low Pb concentrations, but the ICP-SMS requires much less sample volume (approximately 2 mL), needs no sample pretreatment, and therefore is considerably faster and less expensive than the conventional approach. Even though absolute Pb concentrations in two ice samples dating from 1974 and 1852 were very similar (9 and 6 pg g(-1)) their fundamentally different isotopic signature (206Pb/207Pb: 1.169 +/- 0.002 vs 1.147 +/- 0.003) clearly indicates different sources of Pb. The analytical procedures described here, therefore, offer great promise for fingerprinting the predominant sources of atmospheric Pb in polar snow and ice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".