{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007438237,0.0008955128,0.0006787801,0.0005580449,0.000367313,0.0009766741,0.0006921914,0.0007134057,0.01333149],"category_scores_gemma":[0.001186796,0.0003523439,0.0008711464,0.00049731,0.000199039,0.0006109559,0.000714125,0.0009077827,0.002821747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002926663,"about_ca_system_score_gemma":0.0007328396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003682328,"about_ca_topic_score_gemma":0.002751366,"domain_scores_codex":[0.9996922,0.00007288446,0.00001960267,0.00006454946,0.0001070148,0.00004381298],"domain_scores_gemma":[0.9997682,0.0001097019,0.00001928891,0.00001782481,0.00007156988,0.00001332049],"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":[0.0003399262,0.0003428491,0.002567033,0.0009285906,0.000204504,0.0002152208,0.0003078187,0.2630735,0.02355797,0.009709084,0.01609604,0.6826575],"study_design_scores_gemma":[0.00009044573,0.0001297477,0.001144141,0.00006249311,0.00004089933,0.0000971265,0.00007652617,0.9592614,0.01476909,0.002863393,0.02142564,0.00003906648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0183336,0.0003234073,0.960004,0.0001431376,0.00008078324,0.0001703104,0.0004426639,0.01038063,0.01012143],"genre_scores_gemma":[0.1491268,0.0003390084,0.8401939,0.0001011789,0.00002983261,0.0006695999,0.0009279069,0.001296703,0.007315069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01333149,"threshold_uncertainty_score":0.04459834,"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."}}