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, Eileen; Zang, Christian; Esper, Jan; Riechelmann, Dana F. C.; Büntgen, Ulf; Briesch, Martin; Reinig, Frederick; Römer, Philipp; Konter, Oliver; Schmidhalter, Martin; Hartl, Claudia (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