Leaching systematics and matrix elimination for the determination of high‐precision Pb isotope compositions of ocean island basalts
Bibliographic record
Abstract
Ocean island basalts from Hawaii and Kerguelen were analyzed for their Pb isotopic compositions to assess the effect of acid leaching and matrix elimination by Pb anion exchange columns on reproducibility and accuracy. Unleached samples consistently yield Pb isotopic ratios that reflect the incorporation of foreign material. Leaching removes up to 70–80% of the total Pb content of the samples with corresponding weight losses between 35 and 60%. The older and more altered Kerguelen basalts show better external reproducibility than the Hawaiian basalts, which appears to be due to the presence in the Hawaiian samples of more radiogenic contaminants (e.g., seawater Pb, drilling mud, and related alteration phases). All leached samples purified twice on anion exchange columns show more radiogenic Pb isotopic ratios than those processed once. The difference is larger for tholeiitic basalts (Hawaiian and Kerguelen Plateau) than for transitional to alkalic basalts (Kerguelen Archipelago). The small differences in measured ratios of total procedural triplicates reflect differential elimination of residual alteration via leaching and matrix effects. The effectiveness of matrix elimination depends on the specific basalt composition, and tholeiitic basalts (i.e., low Pb concentrations) require two passes on anion exchange columns. This study shows that all steps in sample processing are critical for achieving accurate high‐precision Pb isotopic compositions of ocean island basalts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".