Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events

Beat Knechtle, Katja Weiss, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia Santos Andrade, Volker Scheer, Ivan Cuk, Robert Gajda, Mabliny Thuany

Open source

DOI: 10.1371/journal.pone.0303960
Published: 2024-08-22
Container: PLOS ONE
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0303960,
  title = {Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events},
  author = {Beat Knechtle and Katja Weiss and David Valero and Elias Villiger and Pantelis T. Nikolaidis and Marilia Santos Andrade and Volker Scheer and Ivan Cuk and Robert Gajda and Mabliny Thuany},
  year = {2024},
  journal = {PLOS ONE},
  doi = {10.1371/journal.pone.0303960},
  url = {https://doi.org/10.1371/journal.pone.0303960}
}

RIS

TY  - JOUR
TI  - Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events
AU  - Beat Knechtle
AU  - Katja Weiss
AU  - David Valero
AU  - Elias Villiger
AU  - Pantelis T. Nikolaidis
AU  - Marilia Santos Andrade
AU  - Volker Scheer
AU  - Ivan Cuk
AU  - Robert Gajda
AU  - Mabliny Thuany
PY  - 2024
JO  - PLOS ONE
DO  - 10.1371/journal.pone.0303960
UR  - https://doi.org/10.1371/journal.pone.0303960
ER  -

APA

Knechtle, B., Weiss, K., Valero, D., Villiger, E., Nikolaidis, P. T., Andrade, M. S., Scheer, V., Cuk, I., Gajda, R., & Thuany, M. (2024). Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events. PLOS ONE. https://doi.org/10.1371/journal.pone.0303960

Source records

crossref · retrieved 2026-09-25T22:36:57.386Z