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practical implementation » practical implications (Expand Search)
code presented » model presented (Expand Search), side presented (Expand Search), order presented (Expand Search)
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Distribution of closest heavy-atom—heavy-atom distances over five different MD datasets (S1 Table).
Published 2025Subjects: -
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Complete text output printed to the terminal by the CLT (shown in Fig 2A) of the main text.
Published 2025Subjects: -
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Table 1 - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data
Published 2025Subjects: -
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PyGMT – Accessing and Integrating GMT with Python and the Scientific Python Ecosystem (AGU24, U12B-05)
Published 2024“…</p><p dir="ltr">PyGMT (<a href="https://www.pygmt.org/" target="_blank">https://www.pygmt.org/</a>) wraps around the very fast GMT C code to make it accessible through the Python programming language. …”
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Datasets To EVAL.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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Statistical significance test results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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How RAG work.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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OpenBookQA experimental results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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AI2_ARC experimental results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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TQA experimental results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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E-EVAL experimental results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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TQA Accuracy Comparison Chart on different LLM.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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ScienceQA experimental results.
Published 2025“…<div><p>This paper presents a novel approach to enhancing educational question-answering (Q&A) systems by combining Retrieval-Augmented Generation (RAG) with Large Language Model (LLM) Code Interpreters. …”
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HaPy-Bug – Human Annotated Python Bug Resolution Dataset
Published 2025“…<p dir="ltr">We present HaPy-Bug, a curated dataset of 793 Python source code commits associated with bug fixes, with each line of code annotated by three domain experts. …”
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