Streamlining geoscience data analysis with an LLM-driven workflow

Large Language Models (LLMs) have made significant advancements in natural language processing and human-like response generation. However, training and fine-tuning an LLM to fit the strict requirements in the scope of academic research, such as geoscience, still requires significant computational resources and human expert alignment to ensure the quality and reliability of the generated conten…

Geological object recognition in legacy maps through data augmentation and tr…

Maps are crucial tools in geosciences, providing detailed representations of the spatial distribution and relationships among geological features. Accurate recognition and classification of geological objects within these maps are essential for applications in resource exploration, environmental management, and geological hazard assessment. Along the years, many legacy geological maps have been…

A hybrid knowledge graph for efficient exploration of lithostratigraphic info…

Rocks formed during different geologic time record the diverse evolution of the geosphere and biosphere. In the past decades, substantial geoscience data have been made open access, providing invaluable resources for studying the stratigraphy in different regions and at different scales. However, many open datasets have information recorded in natural language with heterogeneous terminologies, …

Using a 3D heat map to explore the diverse correlations among elements and mi…

This paper presents an enhanced 3D heat map for exploratory data analysis (EDA) of open mineral data, addressing the challenges caused by rapidly evolving datasets and ensuring scientifically meaningful data exploration. The Mindat website, a crowd-sourced database of mineral species, provides a constantly updated open data source via its newly established application programming interface (API…

Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow languag…

Recently, workflow management platforms are gaining more attention in the artificial intelligence (AI) community. Traditionally, researchers self-managed their workflows in a manual and tedious way that heavily relies on their memory. Due to the complexity and unpredictability of AI models, they often struggled to track and manage all the data, steps, and history of the workflow. AI workflows a…

Ontology spectrum for geological data interoperability

Edisi

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ISBN/ISSN

978-90-6164-323-4

Deskripsi Fisik

xiv, 184 hlm. : illus.

Judul Seri

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No. Panggil

DS 551 Xia O