Effects of acid leaching on the Sr‐Nd‐Hf isotopic compositions of ocean island basalts
Bibliographic record
Abstract
The ability to conduct multi‐isotopic analyses (e.g., Sr‐Nd‐Hf‐Pb) on the same sample is critical for studies that evaluate the mantle source components of oceanic basalts. The isotopic compositions of relatively immobile elements, such as Nd (and other REE) and Hf, are considered to be relatively resistant to alteration, however, accurate Sr and Pb isotopic analyses of oceanic basalts require thorough acid leaching prior to dissolution. A detailed study of the Sr, Nd and Hf isotopic systematics of acid‐leached oceanic basalts from Hawaii and Kerguelen was undertaken to assess how acid leaching affects their isotopic compositions. Most of the Sr, Nd and Hf was removed in the first acid leaching steps. Hawaiian basalts lose up to 35% and 40% of their total Sr and Hf contents, respectively, whereas for Kerguelen basalts the corresponding losses are 63% and ∼70%. Acid leaching leads to significant loss of the original Nd content (up to 90%), which cannot be solely explained by the elimination of alteration phases and is likely related to preferential removal of the REE in the constituent silicate minerals (e.g., plagioclase, clinopyroxene). The leached residues yield Sr isotopic ratios significantly less radiogenic than their respective unleached powders and Nd‐Hf isotopic compositions that are within analytical uncertainty of the respective unleached powders. This study shows that multi‐isotopic analyses on the same acid‐leached sample aliquot can produce reliable results for use in the discrimination of mantle source components of oceanic 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".