{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003508338,0.00009489774,0.0002323502,0.0001588693,0.00009085489,0.00005728369,0.0005707509,0.0001343699,0.00000103667],"category_scores_gemma":[0.0004872152,0.00008499423,0.00004392218,0.0001039427,0.00008688049,0.0005799172,0.000161543,0.0001804585,4.439407e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003009444,"about_ca_system_score_gemma":0.00007159294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009237641,"about_ca_topic_score_gemma":0.0000135996,"domain_scores_codex":[0.9990593,0.00001551605,0.0004735692,0.0001789163,0.000129676,0.0001429954],"domain_scores_gemma":[0.9984595,0.00004369354,0.0009114289,0.0002911343,0.0002485238,0.00004570985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001642097,0.000242142,0.01293873,0.0003224135,0.0000384066,0.00002779706,0.001405191,0.0004357447,0.6560503,0.004672584,0.0001466429,0.3235559],"study_design_scores_gemma":[0.002263283,0.0005090532,0.007910223,0.0004291043,0.00002021557,0.0001481851,0.0001727949,0.2067492,0.4878298,0.290419,0.003215268,0.0003337708],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3732633,0.00005941226,0.6199913,0.006194361,0.00008043092,0.0001894701,0.000001162473,0.00001333872,0.0002071785],"genre_scores_gemma":[0.9859008,0.00003090831,0.0139172,0.00001608499,0.00008750371,0.00001694599,0.000003964003,0.000002142311,0.00002446334],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6126375,"threshold_uncertainty_score":0.3465964,"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."}}