Cargando…
Cracking double-blind review: Authorship attribution with deep learning
Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-revi...
Autores principales: | Bauersfeld, Leonard, Romero, Angel, Muglikar, Manasi, Scaramuzza, Davide |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10313031/ https://www.ncbi.nlm.nih.gov/pubmed/37390072 http://dx.doi.org/10.1371/journal.pone.0287611 |
Ejemplares similares
-
Champion-level drone racing using deep reinforcement learning
por: Kaufmann, Elia, et al.
Publicado: (2023) -
Machine learning for authorship attribution and cyber forensics
por: Iqbal, Farkhund, et al.
Publicado: (2020) -
AlphaPilot: autonomous drone racing
por: Foehn, Philipp, et al.
Publicado: (2021) -
The Impact of Double vs. Single-blinded Review on Plastic Surgery Authorship
por: Maisner, Rose, et al.
Publicado: (2022) -
Authorship attribution based on Life-Like Network Automata
por: Machicao, Jeaneth, et al.
Publicado: (2018)