Sibert Elizabeth C.

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Last Name
Sibert
First Name
Elizabeth C.
ORCID
0000-0003-0577-864X

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  • Article
    Applicability of object detection to microfossil research: implications from deep learning models to detect microfossil fish teeth and denticles using YOLO-v7
    (American Geophysical Union, 2024-01-23) Mimura, Kazuhide ; Nakamura, Kentaro ; Yasukawa, Kazutaka ; Sibert, Elizabeth C. ; Ohta, Junichiro ; Kitazawa, T. ; Kato, Yasuhiro
    Microfossils of fish teeth and denticles, referred to as ichthyoliths, provide critical information for depositional ages, paleo-environments, and marine ecosystems, especially in pelagic realms. However, owing to their small size and rarity, it is time-consuming and difficult to analyze large numbers of ichthyoliths from sediment samples, limiting their use in scientific studies. Here, we propose a method to automatically detect ichthyoliths from microscopic images using a deep learning technique. We applied YOLO-v7, one of the latest object detection architectures, and trained several models under different conditions. The model trained under appropriate conditions with an original data set achieved an F1 score of 0.87. We then enhanced the data set efficiently using the pre-trained model. We validated the practical applicability of the model by comparing the number of ichthyoliths detected by the model with those counted manually. This revealed that the best model can predict the number of triangular teeth, denticles and irregularly shaped teeth with minimal human intervention. This object detection method can extend the applicability of deep learning to a wider array of microfossils and has the potential to dramatically increase the spatiotemporal resolution of ichthyolith records for applications across disciplines.
  • Article
    Neurodiversity: An important axis of diversity in ocean sciences
    (Oceanography Society, 2023-12-05) Wilson, Jamie D. ; Sibert, Elizabeth ; Grigoratou, Maria ; Jones, Chloe L.C. ; Rubin, Leah ; Smillie, Zeinab
    Neurodiversity refers to variations in the human brain that affect information processing; it includes conditions, or “neurotypes,” such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), dyslexia, dyscalculia, and dyspraxia, among others. Neurodiversity can be conceptualized as significant differences in the ways that individuals process information; such differences may concern written or verbal language, sensory information, body language, or social interactions. These differences have been historically viewed within the medical model of disability, for example, as deficits in ability through a diagnosed condition, often associated with a goal of curing or managing the condition.