Lomiko Metals Inc. (Lomiko) and Critical Elements Lithium Corporation mandated GoldSpot Discoveries Corp. ("GoldSpot") to conduct a remote targeting process for lithium, on the Bourier claims within the Nemiscau belt. GoldSpot uses cutting edge technology and geoscientific expertise to mitigate exploration risks and make mineral discoveries.

GoldSpot Discoveries Corp.'s proprietary approach of Artificial Intelligence (AI) and geological interpretation highlight lithium potential at Bourier claims within the Nemiscau greenstone belt; A total of 15 high to moderate prospectivity lithium targets were identified. Preliminary Summer 2021 field exploration results have revealed the discovery of five new sectors of spodumene-rich (Li) pegmatites, highlighting the potential of the Bourier project. Critical Elements and Lomiko Metals (Joint Venture) boast a unique and favorable land position for lithium exploration within the Nemiscau Belt.

The study hinged on digital extraction from an exhaustive collection of compiled data, including assessment files, government data and academic studies. This dataset provided outcrop/sample description, bedrock geology, geochemical analyses, and geophysical surveys. Original data was cleaned and combined to create a comprehensive data set for geological interpretation and machine learning processes.

The compilation of discrete outcrop observations allowed a reliable update to existing geologic maps, resulting in a refined lithium exploration-oriented pegmatite map. A total of 99 pegmatite bodies were added to the current geological map, highlighting previously unknown potential for economic lithium mineralization. An up-to-date structural interpretation was created based on a high-resolution aeromagnetic survey commissioned by Critical Elements.

This survey revealed structurally complex patterns, including large-scale folds and major ENE-trending ductile fault zones. Lithium Target Generation - GoldSpot generated lithium targets using a knowledge-based approach with Artificial Intelligence (AI) data-driven methods. Process - The AI data analysis trains machine learning algorithms to predict the presence of lithium using all variables (features), both numeric and interpreted on a 10 x 10 m grid cell datacube.

Once the model performs to a satisfactory level, results produced include: 1) a series of zones with relatively high probability of containing lithium; 2) a ranking of feature importance for each input feature. Performance - The best prediction model for lithium at Bourier was obtained using the Extended Euclidean Algorithm for which performance metric was at 75% precision. The updated lithology and structural interpretation were the dominant contributors to the targeting model.

Field Work and Preliminary Results - In preparation of field work, GoldSpot provided a map of probable outcrop zones, resulting from the AI analysis on high-resolution satellite imagery. The machine learning-assisted outcrop detection allows for time- and cost-efficient field exploration. An exploration crew composed of Critical Elements' and GoldSpot's geoscientists conducted a 20-day prospecting program at the Bourier project, with focus on the high to moderate lithium targets generated by GoldSpot.

The highlights of this program include the discovery of five new sectors of spodumene-rich (Li) pegmatite (laboratory analysis results are pending. These discoveries were made within, or the extension, of GoldSpot's targets.