{"id":"W3036151878","doi":"10.36378/jtos.v3i1.436","title":"IMPLEMENTASI METODE MOORA (MULTI OBJECTIVE OPTIMIZATION ON THE BASIC OF RATIO ANALYSIS) UNTUK REKOMENDASI PEMILIHAN TYPE SEPEDA MOTOR TERBAIK (Studi Kasus : CV. Satu Hati Perkasa)","year":2020,"lang":"en","type":"article","venue":"JURNAL TEKNOLOGI DAN OPEN SOURCE","topic":"Multimedia Learning Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Process (computing); Operating 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001337253,0.0004007247,0.0007843873,0.0002747151,0.0004541605,0.0005226137,0.003184423,0.0001477522,0.0001086306],"category_scores_gemma":[0.001083569,0.0002904835,0.0002394746,0.002336231,0.0001436634,0.0005597987,0.001403603,0.0006102105,0.00005959457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334727,"about_ca_system_score_gemma":0.0002632448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002888531,"about_ca_topic_score_gemma":0.0000586262,"domain_scores_codex":[0.9957654,0.001215729,0.0008254756,0.0009405435,0.0007427401,0.000510086],"domain_scores_gemma":[0.996794,0.0006434011,0.0009034996,0.001068199,0.0003762906,0.0002145448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001479964,0.001278761,0.1293252,0.0002076099,0.008001463,0.0001752946,0.10276,0.655457,0.03877137,0.008133174,0.01059903,0.04381114],"study_design_scores_gemma":[0.001630206,0.001305902,0.01619312,0.00006384365,0.0002948297,0.00001670082,0.006690675,0.9640232,0.005129652,0.00001364128,0.004094715,0.0005434919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2527757,0.0002712892,0.7319408,0.008578069,0.00059018,0.003183214,0.00006577501,0.0004098082,0.002185146],"genre_scores_gemma":[0.9727687,0.00002612475,0.02533224,0.0008589303,0.0001548098,0.00007764036,0.00003506211,0.00004081,0.000705687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.719993,"threshold_uncertainty_score":0.9999548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06371344210926701,"score_gpt":0.3127580142152291,"score_spread":0.2490445721059621,"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."}}