Major element variations in Hawaiian shield lavas: Source features and perspectives from global ocean island basalt (OIB) systematics
Bibliographic record
Abstract
Among volcanic hot spots globally, Hawaii has the highest magma flux, yet there is significant controversy surrounding the composition of the mantle sourcing Hawaiian lavas. In order to place constraints on the source lithologies of Hawaiian lavas, we explore relationships between major elements and radiogenic isotopes in tholeiitic, shield‐building lavas. Olivine‐fractionation corrected lava compositions reveal clear trends between radiogenic isotopes and major elements. Individual data points exhibit remarkable trends and there is no need to average the data by volcano. Data form arrays that are anchored by Koolau lava at one end (with high 87Sr/86Sr, 187Os/188Os, SiO2, and Na2O/TiO2, and low 143Nd/144Nd, 206Pb/204Pb, TiO2, CaO and CaO/Al2O3) and by Kea and Loihi lavas at the other (with low 87Sr/86Sr, 187Os/188Os, SiO2, and Na2O/TiO2, and high 143Nd/144Nd, 206Pb/204Pb, TiO2, CaO and CaO/Al2O3). FeOtotal, Al2O3 and Na2O concentrations do not correlate with radiogenic isotopes. The Hawaiian data set exhibits correlations that mirror the best correlations between major elements and radiogenic isotope in the global ocean island basalt (OIB) database. We suggest that the mechanism driving the correlations in Hawaii illustrates, in microcosm, a larger global process that generates major element variability in mantle plumes. Like the global arrays, the Hawaiian lavas with radiogenic Pb and SiO2‐poor lavas are sourced by a SiO2‐poor mafic component (pyroxenite) admixed with peridotite, while Hawaiian lavas with unradiogenic Pb and high SiO2 are sourced by a SiO2‐rich mafic component (eclogite). The variable SiO2 in the mafic component may result from different degrees of SiO2‐extraction from the slab during subduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".