{"id":"W2600555959","doi":"10.1016/j.gexplo.2017.03.015","title":"Application of spatially weighted technology for mapping intermediate and felsic igneous rocks in Fujian Province, China","year":2017,"lang":"en","type":"article","venue":"Journal of Geochemical Exploration","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Geological Survey of Canada","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Felsic; Geology; Igneous rock; Mineral exploration; Spatial distribution; Spatial analysis; Geologic map; Mineralization (soil science); Mining engineering; Soil science; Physical geography; Geochemistry; Mafic; Remote sensing; Geomorphology; Soil water","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0004761288,0.0004887086,0.0002714912,0.004038699,0.0007371324,0.0005691146,0.0004790437,0.0003552154,0.0004484598],"category_scores_gemma":[0.0007170581,0.0002277141,0.0003037317,0.003606909,0.0002164487,0.000520452,0.0005830287,0.0001146249,0.00007754676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008896078,"about_ca_system_score_gemma":0.001761034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1218558,"about_ca_topic_score_gemma":0.1619726,"domain_scores_codex":[0.9997455,0.00002468165,0.00002349487,0.00008256276,0.00008087789,0.00004282834],"domain_scores_gemma":[0.9996998,0.00004067742,0.00004624449,0.00002388822,0.0001603535,0.00002893465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002143442,0.0001658487,0.6894833,0.0002533934,0.0002968295,0.0008454812,0.00119585,0.05240811,0.06024025,0.001394319,0.0008428057,0.1926595],"study_design_scores_gemma":[0.00003669054,0.000110005,0.7136694,0.00002537904,0.0004291606,0.0002666565,0.001919213,0.2631184,0.01486199,0.001317188,0.004182089,0.00006387633],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916548,0.000223145,0.006604924,0.0000395324,0.000008034987,0.00001694816,0.0003981106,0.000073086,0.0009813815],"genre_scores_gemma":[0.9939892,0.00008150818,0.005252631,0.000004579178,0.000003682022,0.000009130733,0.000278319,0.00000423828,0.0003766493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1218558,"threshold_uncertainty_score":0.2422931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134007241082127,"score_gpt":0.2352026653701375,"score_spread":0.2238625929593162,"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."}}