{"id":"W4402380092","doi":"10.62951/bridge.v2i4.199","title":"Pengelompokan Data Kasus Keracunan Makanan Biologis Berdasarkan Faktor Penyebab Menggunakan Metode Clustering","year":2024,"lang":"en","type":"article","venue":"Bridge","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":"Cluster analysis; Mathematics; Computer science; Statistics","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":[0.0007075405,0.0007595855,0.0007579557,0.002641751,0.0006966777,0.001715814,0.000595407,0.0006054781,0.004649563],"category_scores_gemma":[0.001732058,0.0002681607,0.0005990969,0.003061785,0.0002729722,0.001217079,0.0006882695,0.0005185609,0.001996516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006717287,"about_ca_system_score_gemma":0.0009080954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009101777,"about_ca_topic_score_gemma":0.008823115,"domain_scores_codex":[0.9992567,0.00008180161,0.00009740889,0.0002036379,0.0002873861,0.00007305133],"domain_scores_gemma":[0.9990601,0.0002729938,0.00007228919,0.00009565243,0.0004567994,0.00004217235],"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":[0.001261659,0.0003206525,0.04890698,0.001481826,0.0002557182,0.0012403,0.00138531,0.0242964,0.02058807,0.003265005,0.02428704,0.8727111],"study_design_scores_gemma":[0.0001574402,0.0005816342,0.2728564,0.0006917136,0.0005047161,0.00433365,0.01060629,0.4024422,0.09292901,0.01402669,0.2003681,0.0005021513],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7341382,0.00768363,0.1776733,0.002376638,0.0009381225,0.0005180417,0.02862559,0.005672184,0.04237434],"genre_scores_gemma":[0.8063027,0.003235452,0.1416486,0.0001456989,0.0001157691,0.0003585411,0.03172426,0.0003842497,0.01608468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009101777,"threshold_uncertainty_score":0.01809758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.084808803073497,"score_gpt":0.3475543587852378,"score_spread":0.2627455557117407,"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."}}