Behaviour of gold during partial melting of supra subduction zone mantle wedge
Bibliographic record
Abstract
Gold-rich magmatic-hydrothermal deposits form in atypical convergent plate-margin tectonic settings conducive to melting lithospheric mantle. Deposits tend to be associated with K 2 O-enriched arc magmas, but ultrapotassic magmas are almost never gold-fertile. Our global compilations of chemical compositions of peridotite xenoliths and of near-primary mafic melts reveal that, during mantle-wedge melting, Au typically has a melt/restite partition coefficient like Yb and Na, much higher than Cu and PGEs. Redox-sensitive element ratios in primitive mafic melts in gold provinces typically indicate ƒO 2 conditions like ordinary arc magmas. Lithophile-element ratios in primitive mafic melts reveal that Au-fertile melts form from garnet-undersaturated, hornblende(±phlogopite)-bearing sources within spinel lherzolite facies, constraining most melting to pressures ≲1.8 GPa and temperatures ≲1100°C, below the solidus of monosulphide solid solution (MSS) of typical mantle composition. Therefore, silicate melts acquire metallogenic fertility primarily by selectively dissolving Au in crystalline MSS. We highlight Au enrichment in lithospheric mantle due to zone refining during orogenic thickening of lithospheric mantle, wherein incompatible-element-rich partial melts are generated by hornblende breakdown at its depth limit and migrate into overlying cooler lithospheric mantle. Fractional crystallisation during ascent of these low-degree melts as hornblende + MSS ± phlogopite-bearing metasomatic veins generates enriched domains within the lithospheric mantle. These low-temperature veins selectively re-melt later upon hydration, decompression, or heating, producing Au-fertile melts in extensional and contractional tectonic settings. Our case studies of gold provinces in rift and orogenic stress settings indicate similar mantle melt compositions but magmatic differentiation trends that diverge according to tectonic stress regime. • Source of Au-fertile melts has ƒO 2 conditions similar to ordinary arc magmas. • Gold behaves as moderately incompatible during mantle-wedge melting. • Gold-fertile magmas derive from hornblende(±phlogopite)-bearing sources. • Silicate melt acquires Au fertility by dissolving crystalline MSS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".