Composition of Garnet from the Xianghualing Skarn Sn Deposit, South China: Its Petrogenetic Significance and Exploration Potential
Bibliographic record
Abstract
The Xianghualing skarn Sn deposit in the southwestern part of the southern Hunan Metallogenic Belt is a large Sn deposit in the Nanling area. In this paper, the garnet has been analyzed by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to obtain the concentrations of the major and trace elements. The results reveal that the garnets from the Xianghualing deposit mainly belong to andradite-grossular (grandite) solid solution and are typically richer in Al than in Fe. They show enrichment in heavy rare earth elements (HREEs) and notably lower light rare earth elements (LREEs), and commonly negative Eu anomalies, indicative of a relatively reduced formation environment. The garnets have high Sn concentrations between 2313 ppm and 5766 ppm. It is also evident that there is a positive correlation between Sn and Fe, suggesting that Sn4+ substitutes into the garnets through substituting for Fe3+ in the octahedral position. Combined with previous studies, it can be recognized that the Sn concentrations of garnet in skarn Sn deposits are generally high, whereas the W concentrations are relatively low. This is just the opposite in garnets from skarn W deposits that typically have high W, but low Sn concentrations. In polymetallic skarn deposits with both economic Sn and W, the concentrations of both metals in garnets are relatively high, although varying greatly. Therefore, the Sn and W concentrations in garnets can be used to evaluate a skarn deposit’s potential to produce Sn and (or) W mineralization, which is helpful in exploration.
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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 | 0.002 | 0.001 |
| 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".