{"id":"W2897457301","doi":"10.23919/chicc.2018.8483742","title":"Near-Infrared Spectrum of Coal Origin Identification Based on SVM Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Principal component analysis; Gaussian; Pattern recognition (psychology); Kernel (algebra); Artificial intelligence; Gaussian function; Coal; Computer science; Kernel principal component analysis; Ranking SVM; Algorithm; Mathematics; Engineering; Chemistry; Kernel method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004046367,0.0005688266,0.0006505378,0.0006647513,0.0003230072,0.000493873,0.0004723028,0.0005476562,0.001480294],"category_scores_gemma":[0.0007328721,0.0001491462,0.0005397419,0.0005018957,0.0002337499,0.0006872045,0.0003367439,0.0004377415,0.0006332516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001817751,"about_ca_system_score_gemma":0.0003866887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001094128,"about_ca_topic_score_gemma":0.0007687836,"domain_scores_codex":[0.9997254,0.00004392455,0.00002236356,0.00006685786,0.0001111479,0.0000301586],"domain_scores_gemma":[0.9997627,0.00007006169,0.00002690404,0.0000176543,0.0001106949,0.00001189137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003765163,0.0002191929,0.005218997,0.0002229989,0.00009303225,0.0002049799,0.0001263565,0.1092856,0.08774317,0.004840979,0.00259235,0.7890759],"study_design_scores_gemma":[0.000008356616,0.00007124731,0.002148586,0.000009286063,0.00001890327,0.00009478127,0.00003410089,0.9846253,0.01078989,0.001112712,0.001074532,0.00001228551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06259367,0.0003611185,0.9335014,0.00008740627,0.00007215919,0.00004934415,0.0000510421,0.0007958792,0.002487941],"genre_scores_gemma":[0.7204165,0.0003596588,0.2748055,0.00005803345,0.0000548816,0.0001084865,0.0002369313,0.00006302981,0.003896953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001480294,"threshold_uncertainty_score":0.004952013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01631232055185095,"score_gpt":0.2884163530973854,"score_spread":0.2721040325455344,"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."}}