{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003674535,0.0001424558,0.0001450425,0.00008342168,0.0009956229,0.0004643952,0.002774497,0.00006131254,0.00001355884],"category_scores_gemma":[0.0001418778,0.0001327624,0.00005081138,0.0001472762,0.000100899,0.0006599066,0.0006163237,0.0002304033,0.0002622615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002563215,"about_ca_system_score_gemma":0.00004750421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001985662,"about_ca_topic_score_gemma":0.00001085865,"domain_scores_codex":[0.9988576,0.00002170449,0.0001679386,0.0004415556,0.0002152775,0.0002959061],"domain_scores_gemma":[0.9970554,0.00003516638,0.0001854595,0.002569763,0.00005283256,0.0001013484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006374835,0.0001485843,0.1473124,0.00005707463,0.00003693292,0.00001736501,0.0007282203,0.00007561247,0.001541764,0.1105895,0.01849487,0.7209913],"study_design_scores_gemma":[0.0005022894,0.0001720966,0.7636689,0.00008642786,0.00002143471,0.000056877,0.00004216507,0.04366134,0.003193006,0.002233058,0.1857812,0.0005812649],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7647765,0.00009011633,0.09325103,0.005293571,0.0004820582,0.0003046416,0.00001704813,0.001371923,0.1344131],"genre_scores_gemma":[0.9772356,0.00004501737,0.02104561,0.0001189018,0.00009035734,0.00004197531,0.00001251372,0.00001279992,0.001397272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.72041,"threshold_uncertainty_score":0.7657627,"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."}}