Mineral/melt partition coefficients of oceanic alkali basalts determined on natural samples using laser ablation-inductively coupled plasma-mass spectrometry (LAM-ICP-MS)
Bibliographic record
Abstract
Abstract In order to limit the range of possible differentiation mechanisms and the impact of these processes on the trace element signatures of igneous suites, it is important to be able to predict or model the compositional evolution of the primary and/or parental magmas. Part of the problem in understanding these relationships in basalts from ocean islands is the paucity of reliable trace element mineral/melt partition coefficients, particularly for undersaturated magmas. Consequently, we have measured mineral/groundmass partition coefficients for Rb, Sr, Y, Zr, Nb, Hf, Ta, Th and REE , in situ , in clinopyroxene, Fe-Ti oxides and olivine in primitive basalts from Rarotonga, Cook Islands using LAM-ICP-MS. Analyses of these mafic rocks show high concentrations of most incompatible trace elements (e.g. Sr, Th, Y, REE , etc.) in pyroxenes relative to the other phases, suggesting that fractionation of pyroxenes was the dominant factor in the distribution of these trace elements during crystal fractionation. In such cases, the highly incompatible elements (D <0.01) are Ba, Cs and Nb. Elements that could also be classified as strongly incompatible (D <0.1) are Rb, Ta and Th. The remaining trace elements have bulk partition coefficient values that range from ∼.0.1 up to ∼.0.8 (Sr, Hf, Zr, Y and REE ). Magnetites incorporate greater amounts of Nb and Ta than the titanaugites, and any significant fractionation of magnetite would have affected the bulk distribution of Nb and Ta. The LREE have lower K d values than other REE , with the HREE having K d values close to unity. Consequently, with the separation of titanaugite an overall enrichment of REE in the residual liquid with an increase in the La/Yb ratio is produced.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.034 | 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".