00000cam 2200000zi 4500 9.957831 CaOODSP 20260130105532 m o d f cr mn |||||||| 251128t20252025onc ob f000 0 eng d 9780660799858 CaOODSP eng rda CaOODSP n-cn--- M183-2/9318E-PDF Parsa, M., author. Vector representations of the Canadian Geoscience Foundation model / M. Parsa. [Ottawa] : Geological Survey of Canada, 2025. ©2025 1 online resource (5 pages). text txt rdacontent computer c rdamedia online resource cr rdacarrier Open file, 2816-7155 ; 9318e Issued also in French under title: Représentations vectorielles du modèle fondateur géoscientifique canadien. Includes bibliographical references (page 5). "The Geological Survey of Canada (GSC) has recently developed several data-driven, geospatial predictive models, with more expected to follow. Nevertheless, several challenges hinder the advancement of this initiative. First, the geoscientific phenomena, such as mineralization, targeted by geospatial predictive modelling tasks are typically rare events, which limits the effectiveness of conventional data-driven algorithms. Second, training models from scratch for each task is both resource-intensive and time-consuming. The Canadian Geoscience Foundation model (CGF) offers a promising solution to the aforementioned challenges by providing a general-purpose, adaptable AI model trained on extensive, diverse, pan-Canadian geoscientific datasets"--Page 1. Geospatial data Computer processing. Vector processing (Computer science) Artificial intelligence Geophysical applications Canada. Geological Survey of Canada, issuing body. Représentations vectorielles du modèle fondateur géoscientifique canadien / (CaOODSP)9.957832 Open file (Geological Survey of Canada) 2816-7155 ; 9318e. (CaOODSP)9.506878 PDF 281 KB https://publications.gc.ca/collections/collection_2026/rncan-nrcan/m183-2/M183-2-9318-eng.pdf HTML N/A https://doi.org/10.4095/pxvyky08xx