{"id":"W2803125750","doi":"10.1016/j.infrared.2018.05.018","title":"Geographic origin identification of coal using near-infrared spectroscopy combined with improved random forest method","year":2018,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Postdoctoral Science Foundation of Jiangsu Province; China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Random forest; Support vector machine; Computer science; Pattern recognition (psychology); Coal; Artificial intelligence; Algorithm; Remote sensing; Environmental science; Geology; Chemistry","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":[0.0006584433,0.0006022413,0.0007057606,0.002015072,0.0006066232,0.0004509049,0.0007662781,0.0003648107,0.0009643799],"category_scores_gemma":[0.0006270174,0.0002202568,0.0009597666,0.001067012,0.0001499101,0.0006279154,0.0003242398,0.0003095303,0.0006748812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001255028,"about_ca_system_score_gemma":0.0003853101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004154545,"about_ca_topic_score_gemma":0.005279273,"domain_scores_codex":[0.9995209,0.00007569037,0.00003074308,0.0001817597,0.0001279292,0.00006293543],"domain_scores_gemma":[0.9996578,0.00007880403,0.00003744775,0.00003705749,0.000173168,0.00001576387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006366107,0.0006307326,0.05725178,0.0003314399,0.0003506105,0.0004765271,0.0002268944,0.06385708,0.1831169,0.002179029,0.003093767,0.6878486],"study_design_scores_gemma":[0.00003840424,0.0001306974,0.04678328,0.00001696283,0.0003657755,0.0004589209,0.0001346909,0.9049764,0.04222471,0.001441146,0.003362972,0.00006605999],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2837099,0.000512888,0.7120702,0.00004178099,0.00006091715,0.00008098136,0.0005574564,0.001497728,0.00146821],"genre_scores_gemma":[0.7185479,0.0002028839,0.2766932,0.00002224474,0.00004716299,0.00006258344,0.001958264,0.000118609,0.002347096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004154545,"threshold_uncertainty_score":0.008260727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296989650068864,"score_gpt":0.2942102642141289,"score_spread":0.2812403677134402,"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."}}