{"id":"W4322098178","doi":"10.29100/jipi.v7i4.3237","title":"DATA MINING K-MEDOIDS DAN K-MEANS UNTUK PENGELOMPOKAN POTENSI PRODUKSI KELAPA SAWIT DI INDONESIA","year":2022,"lang":"id","type":"article","venue":"JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Physics; Forestry; Horticulture; Biology; Mathematics; Geography","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","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","open_science"],"category_scores_codex":[0.004337547,0.001650619,0.001697087,0.001414712,0.005737295,0.003041485,0.01379777,0.0004245797,0.0004639473],"category_scores_gemma":[0.0005588021,0.001853297,0.0005157891,0.003403658,0.0006824367,0.006417392,0.01199178,0.00470402,0.0009079329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007264662,"about_ca_system_score_gemma":0.002267136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005835827,"about_ca_topic_score_gemma":0.0001684938,"domain_scores_codex":[0.986373,0.001266152,0.003406855,0.002704598,0.003446368,0.002803017],"domain_scores_gemma":[0.9863313,0.00062215,0.002434961,0.008565458,0.0006203354,0.00142581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001097891,0.003784948,0.06718607,0.002529419,0.002477527,0.001338284,0.1109504,0.01770692,0.001505052,0.02744185,0.4296298,0.3343518],"study_design_scores_gemma":[0.004074429,0.001165274,0.08779285,0.0005660141,0.0004262931,0.002756122,0.01138269,0.2309936,0.000061537,0.00009242314,0.657622,0.003066776],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7667599,0.004448151,0.06554084,0.02631457,0.01005953,0.006191482,0.01389005,0.003532041,0.1032635],"genre_scores_gemma":[0.952764,0.0003181942,0.02292821,0.004269165,0.00165068,0.0005498876,0.01234429,0.0003113936,0.004864201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3312851,"threshold_uncertainty_score":0.99987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02938817875815523,"score_gpt":0.2744174585292508,"score_spread":0.2450292797710955,"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."}}