{"id":"W4399571588","doi":"10.46880/mtk.v10i1.2811","title":"ANALISIS CLUSTERING STUNTING DENGAN DISTANCE EUCLID","year":2024,"lang":"en","type":"article","venue":"METHODIKA Jurnal Teknik Informatika dan Sistem Informasi","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":"Malnutrition; Government (linguistics); Cluster analysis; Cluster (spacecraft); Environmental health; Population; Developing country; Geography; Business; Medicine; Economic growth; Computer science; Artificial intelligence; Economics","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":[0.002033119,0.001460401,0.00135877,0.006657907,0.001148229,0.002115701,0.001189415,0.001074614,0.002377917],"category_scores_gemma":[0.005044641,0.000346927,0.001440456,0.004802623,0.0005360942,0.001171945,0.0009104225,0.0008591473,0.001326794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008382996,"about_ca_system_score_gemma":0.001447041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01653608,"about_ca_topic_score_gemma":0.0107948,"domain_scores_codex":[0.9977635,0.000283388,0.0002952964,0.0004688989,0.0009086342,0.0002802447],"domain_scores_gemma":[0.9969265,0.0007547176,0.0002777802,0.0001993207,0.001715609,0.0001261493],"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":[0.001035615,0.000501992,0.08300649,0.001588103,0.000764424,0.0008934913,0.001469091,0.1105392,0.01698201,0.005023095,0.0214051,0.7567915],"study_design_scores_gemma":[0.00005712809,0.00060067,0.1187918,0.0003887745,0.0002792702,0.002326646,0.003994493,0.8019314,0.02813184,0.008858295,0.0343096,0.0003302389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4331018,0.004957035,0.5425034,0.0008440229,0.0006656591,0.0005283121,0.004048491,0.003598114,0.009753202],"genre_scores_gemma":[0.693449,0.001554676,0.2895938,0.000107684,0.0000779783,0.0003212303,0.008441684,0.0003787318,0.006075132],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01653608,"threshold_uncertainty_score":0.03287965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143787453699805,"score_gpt":0.3044356924922677,"score_spread":0.2829978179552697,"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."}}