MULTICOMPONENT ELEMENTAL AND ISOTOPIC MIXING IN Ni Cu (PGE) ORES AT KAMBALDA, WESTERN AUSTRALIA
Bibliographic record
Abstract
Most magmatic Ni–Cu–(PGE) deposits are considered to have formed from sulfide-undersaturated silicate magmas and to contain a significant component of crustal sulfur that was derived via wholesale melting, partial melting, or devolatilization of wall rocks. Under such circumstances, the system may comprise a silicate magma and a sulfide magma, with or without crystal-line solids, undissolved wallrock-derived xenoliths, an unmixed silicate xenomelt, or an undissolved xenovolatile phase, each of which may contain distinct chalcophile and lithophile components. Because traditional two-component (silicate magma – sulfide magma) mass-balance models do not accurately model such systems, we have developed a series of multicomponent elemental and isotopic mass-balance equations to model batch equilibration in magmatic Ni–Cu–(PGE) systems. We have applied them to the type examples of komatiite-associated Ni–Cu–(PGE) deposits at Kambalda, Western Australia. The calculations indicate that the elemental and isotopic compositions of the various components in a multicomponent system will vary considerably as a function of the relative abundances of the components, and that different metals and isotopic systems may decouple from each other, yielding apparently conflicting information regarding the sources of the components. The results suggest that the S isotopic and Zn compositions of the ores are more sensitive indicators of contamination than the Os isotopes, and support a sediment-melting model for Kambalda.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".