Establishing AI State of Control: An Error Taxonomy and Assurance Framework for Transformer-Based Probabilistic Systems in Regulated Life Sciences

Kayla Britt

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DOI: 10.21203/rs.3.rs-10476545/v1
Published: 2026-10-11
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Publisher: Springer Science and Business Media LLC
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Cite this work

BibTeX

@article{allodium:10.21203/rs.3.rs-10476545/v1,
  title = {Establishing AI State of Control: An Error Taxonomy and Assurance Framework for Transformer-Based Probabilistic Systems in Regulated Life Sciences},
  author = {Kayla Britt},
  year = {2026},
  doi = {10.21203/rs.3.rs-10476545/v1},
  url = {https://doi.org/10.21203/rs.3.rs-10476545/v1}
}

RIS

TY  - JOUR
TI  - Establishing AI State of Control: An Error Taxonomy and Assurance Framework for Transformer-Based Probabilistic Systems in Regulated Life Sciences
AU  - Kayla Britt
PY  - 2026
DO  - 10.21203/rs.3.rs-10476545/v1
UR  - https://doi.org/10.21203/rs.3.rs-10476545/v1
ER  -

APA

Britt, K. (2026). Establishing AI State of Control: An Error Taxonomy and Assurance Framework for Transformer-Based Probabilistic Systems in Regulated Life Sciences. https://doi.org/10.21203/rs.3.rs-10476545/v1

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crossref · retrieved 2026-11-10T14:04:28.724Z