Metal-silicate Partitioning at High Pressure and Temperature: Experimental Methods and a Protocol to Suppress Highly Siderophile Element Inclusions
Bibliographic record
Abstract
Estimates of the primitive upper mantle (PUM) composition reveal a depletion in many of the siderophile (iron-loving) elements, thought to result from their extraction to the core during terrestrial accretion. Experiments to investigate the partitioning of these elements between metal and silicate melts suggest that the PUM composition is best matched if metal-silicate equilibrium occurred at high pressures and temperatures, in a deep magma ocean environment. The behavior of the most highly siderophile elements (HSEs) during this process however, has remained enigmatic. Silicate run-products from HSE solubility experiments are commonly contaminated by dispersed metal inclusions that hinder the measurement of element concentrations in the melt. The resulting uncertainty over the true solubility and metal-silicate partitioning of these elements has made it difficult to predict their expected depletion in PUM. Recently, several studies have employed changes to the experimental design used for high pressure and temperature solubility experiments in order to suppress the formation of metal inclusions. The addition of Au (Re, Os, Ir, Ru experiments) or elemental Si (Pt experiments) to the sample acts to alter either the geometry or rate of sample reduction respectively, in order to avoid transient metal oversaturation of the silicate melt. This contribution outlines procedures for using the piston-cylinder and multi-anvil apparatus to conduct solubility and metal-silicate partitioning experiments respectively. A protocol is also described for the synthesis of uncontaminated run-products from HSE solubility experiments in which the oxygen fugacity is similar to that during terrestrial core-formation. Time-resolved LA-ICP-MS spectra are presented as evidence for the absence of metal-inclusions in run-products from earlier studies, and also confirm that the technique may be extended to investigate Ru. Examples are also given of how these data may be applied.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".