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  1. 21

    E-EVAL experimental results. by Jin Lu (428513)

    Published 2025
    “…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …”
  2. 22

    TQA Accuracy Comparison Chart on different LLM. by Jin Lu (428513)

    Published 2025
    “…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …”
  3. 23

    ScienceQA experimental results. by Jin Lu (428513)

    Published 2025
    “…We evaluated our proposed system on five educational datasets—AI2_ARC, OpenBookQA, E-EVAL, TQA, and ScienceQA—which represent diverse question types and domains. Compared to vanilla Large Language Models (LLMs), our approach combining Retrieval-Augmented Generation (RAG) with Code Interpreters achieved an average accuracy improvement of 10−15 percentage points. …”
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    PTPC-UHT bounce by David Parry (22169299)

    Published 2025
    “…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”
  6. 26

    Data features examined for potential biases. by Harry Hochheiser (3413396)

    Published 2025
    “…Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias.…”
  7. 27

    Analysis topics. by Harry Hochheiser (3413396)

    Published 2025
    “…Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias.…”
  8. 28

    Data for "A hollow fiber membrane permeance evaluation device demonstrating outside-in and inside-out performance differences" by Timothy Warner (20222838)

    Published 2025
    “…</li><li>Figures generated by the python code.</li></ol><h2>Key References</h2><h3>Membrane flux analysis tool:</h3><ul><li>Citation: Warner, Timothy; Mullins, Nathan; de Lannoy, Charles-François (2025). …”
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