{"id":"W4399974175","doi":"10.33772/anoatik.v2i1.30","title":"PENERAPAN METODE NAIVE BAYES UNTUK KLASIFIKASI KATEGORI OLAH PANGAN (STUDI KASUS DINAS KESEHATAN KOTA PALEMBANG)","year":2024,"lang":"id","type":"article","venue":"AnoaTIK Jurnal Teknologi Informasi dan Komputer","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Crosslight Software (Canada)","funders":"","keywords":"Statistics; Mathematics; Psychology","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","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001535848,0.00130626,0.001131068,0.0009846142,0.001369299,0.003563421,0.004145528,0.0006352735,0.00008005929],"category_scores_gemma":[0.0002956642,0.001129031,0.0006471854,0.002364669,0.0005952362,0.003595864,0.002516878,0.002643519,0.001665519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477702,"about_ca_system_score_gemma":0.0008075116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002108424,"about_ca_topic_score_gemma":0.00008161306,"domain_scores_codex":[0.9926695,0.0004316752,0.001773211,0.001929685,0.001201463,0.001994526],"domain_scores_gemma":[0.9949905,0.000832458,0.0006767689,0.002385231,0.0004445025,0.0006705379],"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":[0.00007878389,0.0006645382,0.01150526,0.0009389448,0.001866846,0.001256725,0.01827215,0.000634059,0.001214945,0.1797474,0.1243889,0.6594314],"study_design_scores_gemma":[0.001196378,0.001862562,0.01937127,0.0008694638,0.0003812329,0.001617552,0.001092718,0.07304717,0.002868033,0.001194255,0.8942747,0.002224724],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2916784,0.02942645,0.5341353,0.02970315,0.02126967,0.004083479,0.0007338789,0.0092219,0.07974777],"genre_scores_gemma":[0.946153,0.003349678,0.02742472,0.001647119,0.002398882,0.0002521146,0.0007650631,0.0002157255,0.01779368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7698857,"threshold_uncertainty_score":0.9999689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979618669687207,"score_gpt":0.2763024918371026,"score_spread":0.2565063051402305,"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."}}