Partitioning Behaviors of Cobalt and Manganese along Diverse Melting Paths of Peridotitic and MORB-Like Pyroxenitic Mantle
Bibliographic record
Abstract
Abstract The Co, Mn, Fe, and Ni contents of olivine phenocrysts and host basalts are sensitive to source mantle lithology, which suggests they may be used to constrain the processes of mantle melting and identify basalts formed from non-peridotitic (i.e. pyroxenitic) mantle sources. Here, we use a new comprehensive, forward model involving multiple parameters to simulate partitioning of Co and Mn during partial melting of the mantle in different tectonic settings: (1) polybaric continuous melting of peridotite mantle in mid-ocean ridges can generate melts that show decreasing Co and Mn with increasing degrees of melting so that the mid-ocean ridge basalts (MORBs) contain ~39–84 μg/g Co and ~900–1600 μg/g Mn; (2) flux-melting of the mantle wedge in subduction zones tends to produce a melt that has Co increasing from ~24 to 55 μg/g and Mn from ~500 to 1110 μg/g with increasing temperature; (3) melts produced by isobaric melting of the subcontinental lithospheric mantle are also sensitive to increasing temperature and have ~35–160 μg/g Co and ~800–2600 μg/g Mn; (4) decompression melting of peridotite related to the mantle plume generates melts containing ~45–140 μg/g Co and ~1000–2000 μg/g Mn, and the abundances of these metals decrease with increasing degrees of melting; and (5) partitioning behaviors of Co, Mn, and Ni during decompression melting of MORB-like pyroxenite contrast with those during decompression melting of peridotite due to the different mineralogy and compositions in mantle lithologies, and the MORB-like pyroxenite-derived melt is metal-poor with ~25–60 μg/g Co, ~290–1600 μg/g Mn, and ~160–340 μg/g Ni. Although high-Ni, low-Mn forsteritic olivine phenocrysts and high melt Fe/Mn ratio have been proposed as diagnostic indicators of pyroxenitic components in the mantle, our models show that these features can be also generated by melting of peridotite at greater depth (i.e. a high pressure and temperature). To quantify the effect of high-pressure melting of peridotite on these diagnostic indicators, we modeled the correlations of melt Fe/Mn and olivine Co, Mn, and Ni contents with melting depth along the decompression melting path of a thermal plume. When Fe/Mn ratios of basalts and/or compositions of olivine phenocrysts deviate significantly from our modeled correlation lines, high-pressure melting of peridotite cannot explain these data, and the existence of pyroxenitic component in the mantle source is likely required. The pyroxenite-derived melt is modeled to be Ni-poor, but mixing with a peridotite-derived melt can strongly increase the partition coefficient of Ni between olivine and mixed melt, resulting in the generation of high-Ni olivine phenocrysts in plume-associated magmatic suites.
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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) | 0.000 | 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) | 0.003 | 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".