Publications by authors named "Takuya Itaki"

Tire and road wear particles (TRWP) are the largest source of microplastics. These particles are generated through friction between tires and road surfaces during use, enter aquatic environments, and may affect aquatic organisms. In this study, TRWP in sediments from 11 different water bodies in Japan were analyzed using pyrolysis gas chromatography/mass spectrometry.

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While deep learning techniques, especially image classification using deep learning, continue to evolve, it has been noted that there is a large time gap in applying these techniques in geological studies. Recently, a new architecture called the vision transformer (ViT), which is an alternative to convolutional neural networks (CNN), has attracted considerable attention. In addition, it has been proposed that the pre-training of classification models using mathematically generated images instead of real images, called formula-driven supervised learning (FDSL), achieves a comparative or even higher performance in visual understanding.

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This study describes a sediment sampling protocol using a Kinoshita-type grab (K-grab) sediment sampler to collect and analyze microplastics (<5 mm) and macroplastics (>5 mm) in marine sediments. During the GB24 geological survey cruise aboard the , 133 surface sediment samples were collected from depths of 20-800 m. The K-grab, equipped with a head-slide weight mechanism, enhanced sampling efficiency across various sediment types.

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Hadal trenches are unique geological and ecological systems located along subduction zones. Earthquake-triggered turbidites act as efficient transport pathways of organic carbon (OC), yet remineralization and transformation of OC in these systems are not comprehensively understood. Here we measure concentrations and stable- and radiocarbon isotope signatures of dissolved organic and inorganic carbon (DOC, DIC) in the subsurface sediment interstitial water along the Japan Trench axis collected during the IODP Expedition 386.

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The Totten Glacier in East Antarctica, with an ice volume equivalent to >3.5 m of global sea-level rise, is grounded below sea level and, therefore, vulnerable to ocean forcing. Here, we use bathymetric and oceanographic observations from previously unsampled parts of the Totten continental shelf to reveal on-shelf warm water pathways defined by deep topographic features.

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The Last Interglacial (LIG: 130,000-115,000 years ago) was a period of warmer global mean temperatures and higher and more variable sea levels than the Holocene (11,700-0 years ago). Therefore, a better understanding of Antarctic ice-sheet dynamics during this interval would provide valuable insights for projecting sea-level change in future warming scenarios. Here we present a high-resolution record constraining ice-sheet changes in the Wilkes Subglacial Basin (WSB) of East Antarctica during the LIG, based on analysis of sediment provenance and an ice melt proxy in a marine sediment core retrieved from the Wilkes Land margin.

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Unlabelled: The fluctuating position of the boundary between the Kuroshio (warm) and Oyashio (cold) currents in the mid-latitude western North Pacific affects both heat transport and air-ocean interactions and has significant consequences for the East Asian climate. We reconstruct the paleoceanography of Marine Isotope Stages (MIS) 20-18, MIS 19 being one of the closest astronomical analogues to the present interglacial, through multiple proxies including microfossil assemblage data, planktonic foraminiferal isotopes (δO and δC), and foraminiferal Mg/Ca-based temperature records, from the Chiba composite section (CbCS) exposed on the Boso Peninsula, east-central Japan. Principal component analysis (PCA) is used to capture dominant patterns of the temporal variation in these marine records, and shows that the relative abundances of calcareous nannofossil and radiolarian taxa are consistent with the water mass types inferred from geochemical proxies.

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Microfossils are a powerful tool in earth sciences, and they have been widely used for the determination of geological age and in paleoenvironmental studies. However, the identification of fossil species requires considerable time and labor by experts with extensive knowledge and experience. In this study, we successfully automated the acquisition of microfossil data using an artificial intelligence system that employs a computer-controlled microscope and deep learning methods.

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