The Na Bop-Pu Sap Pb-Zn ore bodies represent a typical vein-type lead-zinc deposit situated in the Cho Don area and are currently being extracted for their lead and zinc resources. This deposit is characterized by its significant scale and quality and is considered one of the prominent lead-zinc deposits in the Cho Don area. Despite its significance, this deposit has not received adequate attention, resulting in limited knowledge of its geology, mineralization, and deposit genesis model. To address this knowledge gap, our study utilized several methodologies, including field surveying, ore mineral analysis under a microscope, and S and Pb isotopic geochemistry. By employing these approaches, we were able to obtain specific insights into the origin of mineralization and the deposit model. Our field survey suggests that the ore deposits are formed as Pb-Zn-bearing veins along Devonian shale, claystone, and limestone faults. Microscopic analyses of the veins reveal the presence of galena, sphalerite, chalcopyrite, pyrite, arsenopyrite, and pyrrhotite as ore minerals, and quartz, calcite, dolomite, and chalcedony as gangue minerals. Sulfur-isotope values (δ34SCDT) of galena 5.3 to 0.1‰ (average 2.8‰), sphalerite 6.8 to 2.5‰ (average 5.3‰), and pyrite 5.8 to 4.1‰ (average 4.9‰) indicate that the sulfide mineralization may be related to a deep source, possibly originating from magmatic activity in the region and contaminated by carbonate-bearing marine sedimentary rocks. Lead-isotope studies indicate a model age of 598-424 Ma for the lead reservoir, consistent with the possible presence of local source rocks containing sulfur. The lead and sulfur in the ore veins were probably contaminated by Devonian carbonate-bearing marine sedimentary rocks and leached from Neoproterozoic to Cambrian magmatic activity. The lead-zinc deposits in Na Bop-Pu Sap do not display any Mississippi valley-type (MVT) or Sedimentary exhalative (SEDEX) lead-zinc deposit characteristics, as they appear to be related to shear zone-hosted lead-zinc deposits.
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This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts
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