Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers

Publikationsart: Zeitschriftenbeiträge (peer-reviewed)
Titel: Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers
Medien: Ecosphere
DOI: 10.1002/ecs2.4453
Band: 14
Artikelnummer: e4453
Autoren: Eileen Kuhl, Prof. Dr. Christian Zang , Jan Esper, Dana F. C. Riechelmann, Ulf Büntgen, Martin Briesch, Frederick Reinig, Philipp Römer, Oliver Konter, Martin Schmidhalter, Claudia Hartl
Veröffentlichungsdatum: 07.03.2023
Projekttitel: Hysterese-Effekte in Bayerischen Buchenwald-Ökosystemen durch Klimaextreme (HyBBEx)
Zitation: Kuhl, E.; Zang, C.; Esper, J.; Riechelmann, D.; Büntgen, U.; Briesch, M.; Reinig, F.; Römer, P.; Konter, O.; Schmidhalter, M.; Hartl, C. (2023): Using machine learning on tree-ring data to determine the geographical provenance of historical construction timbers. Ecosphere 14, e4453. DOI: 10.1002/ecs2.4453