{"id":"W3135071512","doi":"","title":"Pengelompokan Provinsi di Indonesia Menggunakan Algoritma Partitioning Around Medoids (PAM) Terhadap Indikator Pembentuk Indeks Pembangunan Manusia (IPM) Tahun 2020","year":2021,"lang":"id","type":"article","venue":"","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medoid; Quarter (Canadian coin); Liberian dollar; Cluster analysis; Geography; Business; Statistics; Mathematics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001280043,0.001066612,0.001037675,0.000344986,0.001711221,0.002879498,0.002378895,0.0005421825,0.0006555198],"category_scores_gemma":[0.000303201,0.001139069,0.0003897818,0.002150265,0.0003616852,0.00168879,0.002139427,0.001870037,0.001105964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003107172,"about_ca_system_score_gemma":0.001574235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005523234,"about_ca_topic_score_gemma":0.0005359072,"domain_scores_codex":[0.9916462,0.0008721805,0.001488987,0.002744343,0.001558465,0.001689825],"domain_scores_gemma":[0.9942673,0.0004280006,0.0007874444,0.002948488,0.0006013951,0.0009673202],"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":[0.0001766103,0.006967114,0.2964424,0.001498539,0.002337946,0.003515178,0.0384045,0.001240849,0.01159938,0.1385194,0.1026569,0.3966412],"study_design_scores_gemma":[0.00671375,0.001364784,0.465183,0.001171588,0.0009222736,0.001922174,0.01133698,0.135901,0.01128882,0.001733818,0.3562957,0.006166106],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7076405,0.003188971,0.1946776,0.0208851,0.004194569,0.002557583,0.000294509,0.002559557,0.06400168],"genre_scores_gemma":[0.9568505,0.0002208084,0.02469206,0.001404809,0.001045897,0.0003618069,0.001158786,0.0001492617,0.01411607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3904751,"threshold_uncertainty_score":0.9996718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479335152922614,"score_gpt":0.2590098776813126,"score_spread":0.2442165261520865,"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."}}