Insights into the quantification and reporting of model-related uncertainty across different disciplines

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate...

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Detalles Bibliográficos
Autores principales: Simmonds, Emily G., Adjei, Kwaku Peprah, Andersen, Christoffer Wold, Hetle Aspheim, Janne Cathrin, Battistin, Claudia, Bulso, Nicola, Christensen, Hannah M., Cretois, Benjamin, Cubero, Ryan, Davidovich, Iván A., Dickel, Lisa, Dunn, Benjamin, Dunn-Sigouin, Etienne, Dyrstad, Karin, Einum, Sigurd, Giglio, Donata, Gjerløw, Haakon, Godefroidt, Amélie, González-Gil, Ricardo, Gonzalo Cogno, Soledad, Große, Fabian, Halloran, Paul, Jensen, Mari F., Kennedy, John James, Langsæther, Peter Egge, Laverick, Jack H., Lederberger, Debora, Li, Camille, Mandeville, Elizabeth G., Mandeville, Caitlin, Moe, Espen, Navarro Schröder, Tobias, Nunan, David, Sicacha-Parada, Jorge, Simpson, Melanie Rae, Skarstein, Emma Sofie, Spensberger, Clemens, Stevens, Richard, Subramanian, Aneesh C., Svendsen, Lea, Theisen, Ole Magnus, Watret, Connor, O’Hara, Robert B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9712693/
https://www.ncbi.nlm.nih.gov/pubmed/36465136
http://dx.doi.org/10.1016/j.isci.2022.105512

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