{"id":"W4252526737","doi":"10.4095/223861","title":"Analysis of improved government geological map information for mineral exploration: incorporating efficiency, productivity, effectiveness, and risk considerations","year":2007,"lang":"en","type":"report","venue":"","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Productivity; Mineral exploration; Government (linguistics); Geologic map; Mining engineering; Geology; Computer science; Environmental resource management; Risk analysis (engineering); Data mining; Environmental science; Business; Geochemistry; Economics; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01896704,0.0006949804,0.001516037,0.008378924,0.0006893921,0.005835898,0.001162423,0.0009727191,0.00336848],"category_scores_gemma":[0.1036688,0.0006070565,0.001711893,0.01372998,0.00129276,0.006591883,0.001781724,0.0009521069,0.0002921066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01119862,"about_ca_system_score_gemma":0.006821328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06547615,"about_ca_topic_score_gemma":0.05733864,"domain_scores_codex":[0.9824103,0.009788387,0.0007642744,0.0007519678,0.005345243,0.0009398289],"domain_scores_gemma":[0.8657085,0.1128791,0.005248479,0.004759501,0.01078336,0.0006211245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006576309,0.0004659305,0.1251177,0.0004197315,0.0004447516,0.00023954,0.0003894487,0.7294169,0.0008290824,0.01709687,0.001852655,0.1230697],"study_design_scores_gemma":[0.00006422468,0.000330155,0.05679875,0.00007607547,0.0001988779,0.00007389663,0.001002945,0.9300771,0.001683368,0.007049842,0.002576218,0.00006850165],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9340107,0.0008678394,0.04331918,0.001646444,0.00003653628,0.0005092601,0.0028339,0.0003934062,0.01638262],"genre_scores_gemma":[0.9616605,0.000532065,0.03429059,0.00005417067,0.00002552037,0.0001302973,0.001831808,0.0001051561,0.00136981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06547615,"threshold_uncertainty_score":0.1301901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02801364400288859,"score_gpt":0.2654379306347389,"score_spread":0.2374242866318503,"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."}}