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25 This work applied a machine learning (ML) method to classify the chemical compositions of 26 apatites from both fertile and barren rocks, aiming to help determine the mineralization …
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Apatite begins to be liberated (≈50% liberation) for particle sizes below 800 μm with maximum liberation at a range size between 40 µm and 400 µm. The mineralogical …
Apatites crystallized from different types of igneous rocks show significant variations in the abundances of some minor and trace elements. In this study, electron probe …
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Apatite's ubiquity in crystalline rocks, variable trace element contents (particularly with regard to the REE, actinides and Sr), and amenability to various dating techniques based …
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This article aims to employ machine learning methods to investigate whether trace elements of magmatic apatites can be used to accurately discriminate different types of magmatic rocks. …
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Apatite major and trace element chemistry is a widely used tracer of mineralization as it sensitively records the characteristics of the magmatic-hydrothermal system at the time of its …
Low temperature apatite (U-Th-Sm)/He (AHe) thermochronology is widely used in Earth Sciences in order to reconstruct the thermal history of rocks, and to quantify uplift and …
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Keywords: Apatite, major and trace element, machine learning, mineralization potential, XGBoost Introduction Apatite (Ca 5 [PO 4] 3 [F,Cl,OH]) is a ubiquitous accessory min - eral in most igneous and metamorphic rocks and derived clastic sediments and is relatively resistant to weathering (O'Sullivan et al. 2020).
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To address this issue, we use a supervised machine-learning-based approach (eXtreme Gradient Boosting, XGBoost) to correlate apatite compositions with ore deposit type, …
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Seven machine learning algorithms along with eleven preoperative variables, were used to construct the prediction model. Subsequently, Model performance was evaluated by calculating AUC and AUPR for subjects in the validation set. On the validation set, all seven machine learning models demonstrated strong discrimination (AUC: 0.687-0.947).
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