{"id":"W4399542840","doi":"10.62828/jpb.v3i2.104","title":"8. SEGMENTASI TINGGI BADAN DAN BERAT BADAN KADET MAHASISWA MENGGUNAKAN K-MEANS CLUSTERING","year":2024,"lang":"id","type":"article","venue":"TNI Angkatan Udara","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Humanities; Physics; Philosophy","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001191988,0.0007860455,0.0006178498,0.0005138476,0.001027871,0.003091241,0.002374807,0.0002849826,0.0001932103],"category_scores_gemma":[0.00009496148,0.0008232835,0.0002998634,0.001691548,0.0002648658,0.001079853,0.001528036,0.001422001,0.001929936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00038525,"about_ca_system_score_gemma":0.0004514635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004912344,"about_ca_topic_score_gemma":0.0002804514,"domain_scores_codex":[0.9947628,0.0003367124,0.0009070746,0.001988564,0.0007601524,0.001244706],"domain_scores_gemma":[0.996585,0.0003355572,0.000220881,0.002285447,0.00009736069,0.0004758244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005764254,0.0006293593,0.001927977,0.001454962,0.0009808077,0.0008191132,0.01902834,0.0007048792,0.01194481,0.07071549,0.1963555,0.6953812],"study_design_scores_gemma":[0.0005906841,0.0002946685,0.002201208,0.001246276,0.0002591564,0.0002916973,0.001047755,0.2328097,0.0009953032,0.0003634242,0.7587297,0.001170407],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06061799,0.01424256,0.7366104,0.03067894,0.0108142,0.002295085,0.001246484,0.006246344,0.137248],"genre_scores_gemma":[0.9218783,0.0003604037,0.03586974,0.001850231,0.001449347,0.0001858565,0.001070969,0.0002804204,0.0370547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8612604,"threshold_uncertainty_score":0.9994218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682313875548924,"score_gpt":0.2804938767749291,"score_spread":0.2636707380194399,"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."}}