{"id":"W6929665040","doi":"10.5066/p970gdd5","title":"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)","year":2025,"lang":"en","type":"dataset","venue":"USGS DOI Tool Production Environment","topic":"Mathematics Education and Teaching Techniques","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"","keywords":"Prospectivity mapping; Geological survey; Mineral exploration; Geographic information system; Resource (disambiguation); Raster data; Raster graphics; Decision support system; Data management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002593069,0.0001547346,0.0002073329,0.0001094585,0.0004388367,0.00006890983,0.000588673,0.0001118662,0.00008876379],"category_scores_gemma":[0.002435901,0.0001131233,0.00001467454,0.0001543782,0.0007927761,0.0001472813,0.0006991507,0.0003164673,2.242334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002081519,"about_ca_system_score_gemma":0.0005284512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.139193,"about_ca_topic_score_gemma":0.1192429,"domain_scores_codex":[0.9979085,0.0003246215,0.0003718759,0.0005275651,0.0006896283,0.0001778305],"domain_scores_gemma":[0.998113,0.0009586045,0.0001856271,0.0006284464,0.00007395013,0.00004040158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008833697,0.0001013355,0.0001526312,0.0001144962,0.00002880188,2.03967e-7,0.0004977028,0.00002375796,0.000004348779,0.0009455889,0.9980574,0.00006492401],"study_design_scores_gemma":[0.0001197755,0.00001415291,0.003373652,0.00003741756,0.00005199036,0.000001767498,0.0005016992,0.0003531611,0.000003468919,0.002957284,0.9924615,0.0001241552],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00423077,0.0001289001,0.0003196152,0.06362595,0.0002990148,0.001833212,0.9294256,0.00001384938,0.0001230989],"genre_scores_gemma":[0.005815489,0.001157169,0.003079826,0.001622588,0.0005700434,0.0002412466,0.9823556,0.00001128642,0.005146768],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06200336,"threshold_uncertainty_score":0.8968287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1130027123047123,"score_gpt":0.3695167620784021,"score_spread":0.2565140497736899,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}