The Role of External Sulfur in Triggering Sulfide Immiscibility at Depth: Evidence from the Huangshan-Jingerquan Ni-Cu Metallogenic Belt, NW China
Bibliographic record
Abstract
Abstract The Huangshan-Jingerquan belt in Northwest China is one of the most important orogen-hosted magmatic Ni-Cu sulfide metallogenic belts worldwide. The Huangshandong, Huangshan, and Tulaergen deposits are the three largest Ni-Cu deposits along the belt. The orebodies are situated inside mafic-ultramafic complexes. Sulfur isotope data and trace element composition of the sulfide ores and country rocks are used to evaluate the importance of crustal S addition for sulfide mineralization and speculate as to the source of the sulfur that triggered sulfide segregation. The S/Se ratios of >95% of the sulfide ores range from 2,398 to 85,222—higher than that of the mantle (2,850–4,350) but lower than the country rocks (S/Se = 3,889–160,769). The sulfide ores from the Huangshandong, Huangshan, and Tulaergen deposits have restricted δ34S values ranging from –0.86 to 1.33, 0.26 to 0.75, and –0.2 to 1.4‰, respectively. However, the country rocks of these mafic-ultramafic complexes have highly heterogeneous δ34S, ranging from –22.3 to 18.8, –22.3 to 2.12, and –1.4 to 5.3‰, respectively. Arsenic/bismuth and Sb/Bi ratios of the sulfide ores range from 0.22 to 7.59 and 0.02 to 2.88, respectively, which are lower than those of the country rocks (3.17–243 and 2.8–33) and mid-ocean ridge basalt (MORB) (5.09–127 and 0.51–9.25). The values of δ34S and S/Se as well as ratios of As/Bi and Sb/Bi of the sulfide ores indicate that the sulfide segregation and consequently the formation of the deposits were closely associated with the addition of crustal sulfur, whereas the sulfides in the country rocks have not been incorporated into the mineralization. Thus, it is proposed that assimilation of external crustal sulfur at depth might play a critical role in triggering sulfide immiscibility and the formation of the magmatic Ni-Cu deposits in the Huangshan-Jingerquan belt.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".