Contamination Artifacts

Contamination Artifacts is a type of artifact in data analysis and machine learning research that arises when extraneous, unintended, or spurious information inadvertently influences a dataset, model, or experimental outcome, thereby compromising the validity, generalizability, or interpretability of results due to the presence of such confounding elements.

11

Publications

535

Citations

38

Authors

15

Institutions

Publications per year

2017–2026

2

Authors

38

  1. TS
    Tyler S. Quist

    University of Utah, United States

    2 publications · 48 citations

    2

    h-index

  2. TJ
    Timothy J. Dahlem

    University of Utah, United States

    2 publications · 48 citations

    2

    h-index

  3. JL
    Joshua L. Bonkowsky

    University of Utah, United States

    2 publications · 48 citations

    2

    h-index

  4. TJ
    Tamara J. Stevenson

    University of Utah, United States

    2 publications · 48 citations

    2

    h-index

  5. LX
    Lingyan Xing

    Nantong University, China

    2 publications · 48 citations

    2

    h-index

1–5 of 38

Venues

  1. Journal of Visualized Experiments

    2 publications · 48 citations

    2

    h-index

  2. Journal of Agricultural and Food Chemistry

    1 publications · 146 citations

    1

    h-index

  3. Economic Theory

    1 publications · 51 citations

    1

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 86 citations

    1

    h-index

  5. Lithos

    1 publications · 25 citations

    1

    h-index

1–5