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Cited by 7

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Predicting the effects of automation reliability rates on human-automation team performance

Hillesheim, Anthony J., Rusnock, Christina F.·2016·2016 Winter Simulation Conference (WSC)

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Research works printed but not linked: 11

  1. 10.1518/001872007779598082 · DOI · Paper page
  2. scerbo, 1996, Theoretical Perspectives on Adaptive Automation, Automation and Human performance theory and applications, 37
  3. singh, 2009, Effects of Automation Reliability and Training on Automation-Induced Complacency and Perceived Mental Workload, Journal of the Indian Academy of Applied Psychology, 35, 9
  4. 10.1518/001872007X215656 · DOI · Paper page
  5. 10.1518/001872006778606822 · DOI · Paper page
  6. 10.1518/hfes.46.1.50.30392 · doi:10.1518/hfes.46.1.50.30392 · Paper page
  7. 10.1177/0018720814547570 · DOI · Paper page
  8. 10.21236/ADA508882 · DOI
  9. bindewald, 2016, Clustering-Based Online Player Modeling, International Joint Conference on Artificial Intelligence (IJCAI) - Computer Games Workshop
  10. 10.1016/j.ijhcs.2014.07.004 · DOI
  11. 10.1145/975817.975844 · DOI · Paper page

Cited by

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Metadata

Authors
Hillesheim, A. J., & Rusnock, C. F.
Venue
2016 Winter Simulation Conference (WSC)
Publisher
IEEE
Published
2016-12
Pages
1802-1813
Cited by
7
References
3

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CoChat. (2026). Citation check: Predicting the effects of automation reliability rates on human-automation team performance. https://app.cochat.ai/papers/10.1109/wsc.2016.7822227
The paper
Hillesheim, A. J., & Rusnock, C. F. (2016). Predicting the effects of automation reliability rates on human-automation team performance. 2016 Winter Simulation Conference (WSC). https://doi.org/10.1109/wsc.2016.7822227

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.