{"id":"W2767913911","doi":"10.31253/te.v1i1.20","title":"Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan","year":2017,"lang":"en","type":"article","venue":"Tech-E","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Poverty; Fuzzy inference system; Fuzzy logic; Meaning (existential); Statistics; Mathematics; Computer science; Econometrics; Psychology; Economics; Artificial intelligence; Economic growth; Fuzzy control system; Adaptive neuro fuzzy inference system","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":[0.001067465,0.0003349661,0.0004039907,0.00246261,0.0006507446,0.001463274,0.0002818411,0.0003185218,0.003667752],"category_scores_gemma":[0.002598841,0.0001292338,0.0003972945,0.001782236,0.0003586699,0.0005282162,0.0004224847,0.0003287497,0.0007970963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007669824,"about_ca_system_score_gemma":0.0004787776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065942,"about_ca_topic_score_gemma":0.009905469,"domain_scores_codex":[0.9990129,0.0001229236,0.00008689354,0.0001294627,0.0005505751,0.0000971636],"domain_scores_gemma":[0.9985058,0.0004957665,0.0001018813,0.00008184724,0.0007514404,0.00006330475],"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":[0.001742706,0.000462048,0.4337852,0.0008391188,0.0003303066,0.001614706,0.008096805,0.03800433,0.02684872,0.006970662,0.008061608,0.4732437],"study_design_scores_gemma":[0.00003315253,0.0009102378,0.7308337,0.0002578089,0.0003019266,0.00154777,0.01600307,0.1993651,0.02457683,0.003856419,0.02217222,0.0001417835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770139,0.0003913243,0.005467572,0.0001995607,0.00004330546,0.00004099231,0.0004648507,0.0001288662,0.01624953],"genre_scores_gemma":[0.994265,0.0001518114,0.002819952,0.00001458327,0.000007697997,0.00002138832,0.00037178,0.00000822909,0.00233951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01065942,"threshold_uncertainty_score":0.02119476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829372997032636,"score_gpt":0.2775414796260565,"score_spread":0.2592477496557301,"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."}}