National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative (ver 1.1, March 2025)
Bibliographic record
Abstract
National-scale geologic, geophysical, and mineral resource raster and vector data covering the United States, Canada, and Australia are provided in this data release. The data were compiled as part of the tri-national Critical Minerals Mapping Initiative (CMMI). The CMMI, established in 2019, is an international science collaboration between the U.S. Geological Survey (USGS), Geoscience Australia (GA), and the Geological Survey of Canada (GSC). One aspect of the CMMI is to use national- to global-scale earth science data to map where critical mineral prospectivity may exist using advanced machine learning approaches (Kelley, 2020). The geoscience information presented in this report include the training and evidential layers that cover all three countries and underpin the resultant prospectivity models for basin-hosted Pb-Zn mineralization described in Lawley and others (2022). It is expected that these data layers will be useful to many regional- to continental-scale studies related to a wide range of earth science research. Therefore, the data layers are organized using widely accepted GIS formats in the same map projection to increase efficiency and effectiveness of future studies. All datasets have a common geographic projection in decimal degrees using a WGS84 datum. Data for the various training and evidential layers were either derived for this study or were extracted from previous national to global-scale compilations. Data from outside work are provided here as a courtesy for completeness of the model and should be cited as the original source. Original references are provided on each child page. Data for the United States were merged to data for Canada to provide composite data that allow for continuity and seamless analyses of the earth science data across the two countries. Earth science data provided in this report include training data for the models. Training data include a mineral resource database of Pb-Zn deposits and occurrences related to either carbonate-hosted (Mississippi Valley type-MVT) or clastic-dominated (aka sedex) Pb-Zn mineralization. Evidential layers that were used as input to the models include GeoTIFF grid files consisting of ground, airborne, and satellite geophysical data (magnetic, gravity, tomography, seismic) and several related derivative products. Geologic layers incorporated into the models include shapefiles of modified lithology and faults for the United States, Canada and Australia. A global database of ancient and modern passive margins is provided here as well as a link to a database mapping the global distribution of black shale units from a previous USGS study. GeoTIFF grids of the final prospectivity models for MVT and for clastic-dominated Pb-Zn mineralization across the US, Canada, and Australia from Lawley and others (2021) are also included. Each child page describes the particular data layer and related derivative products if applicable. Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020–3035, 2 p., https://doi.org/10.3133/fs20203035. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".