Search for: All records

Understanding the petrological and geochemical processes shaping the Moho transition zone (MTZ) is crucial for advancing our knowledge of thermal and chemical exchanges between the oceanic crust and the residual upper mantle. In this study, we systematically investigate the MTZ outcropped within the Zedong ophiolite, located in the eastern part of the Yarlung-Tsangpo Suture Zone (YTSZ), with the aim of at reconstructing the magmatic processes responsible for generating the petrological Moho. The Zedong MTZ comprises a sequence of dunite, wehrlite, pyroxenite, and gabbro, with frequent occurrences of clinopyroxene-rich lithologies. Cyclicity within the MTZ sequences is characterized by the recurrence of olivine-rich intervals and the presence of zig-zag patterns in both major and trace elements of clinopyroxenes. Zircon Usingle bondPb dating on the Zedong gabbros supports the coeval formation of the Zedong ophiolite with other YTSZ ophiolites. Clinopyroxene in the Zedong MTZ follows a differentiation sequence characterized by an increase in contents of Al2O3 and TiO2, coupled with a decrease in Mg#. This differentiation sequence along with frequent occurrences of amphibole suggest the evolution of a primitive hydrous melt depleted in Al2O3, TiO2, and Na2O. The depleted Ndsingle bondHf isotopes and rare earth element patterns of the MTZ rocks indicate that their parental magmas originated from fluid-enhanced re-melting of a previously depleted mantle. Additionally, we proposed that the initiation of a new subduction zone results in the re-melting of the mantle peridotite, leading to the formation of primitive hydrous basaltic melts. The variable lithologies observed in the Zedong MTZ arise from fractional crystallization and repeated replenishment of hydrous melts. 

Data-driven algorithms are only as good as the data they work with, while datasets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons, ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that “bias in, bias out,” one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This article reviews the literature on identifying and resolving representation bias as a feature of a dataset, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains. 

Gradient flow; lattice QCD scale setting