{"id":"W7155014346","doi":"10.32493/jtsi.v8i2.43780","title":"Prediksi Kerentanan Kekeringan Perkotaan Menggunakan Machine Learning: Pendekatan untuk Perencanaan Kota Tangguh di Kota Kupang","year":2025,"lang":"","type":"article","venue":"Jurnal Teknologi Sistem Informasi dan Aplikasi","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Vulnerability (computing); Population; Climate change; Sustainable development; Population growth; Vulnerability assessment; Vulnerability index","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":[0.0005487577,0.0004270898,0.0003035167,0.0004103221,0.0004263291,0.001372542,0.0003108383,0.0002996718,0.001655663],"category_scores_gemma":[0.0009511499,0.0001421827,0.0002634625,0.0005327472,0.0002559171,0.001005232,0.0005773029,0.0004989409,0.0003341161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000588957,"about_ca_system_score_gemma":0.001335726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007164464,"about_ca_topic_score_gemma":0.01111416,"domain_scores_codex":[0.9998185,0.00006300322,0.00001270339,0.00004221933,0.00002993046,0.00003363198],"domain_scores_gemma":[0.9997625,0.0001411703,0.00001716706,0.000008048588,0.00005508395,0.00001608577],"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":[0.0003990069,0.000362902,0.06876378,0.0007329434,0.0001730639,0.0009140738,0.001764727,0.1728178,0.009064667,0.01824475,0.003702402,0.72306],"study_design_scores_gemma":[0.00004594147,0.0003177618,0.05121375,0.0005137403,0.0002474496,0.0005275287,0.004354001,0.8670957,0.01653548,0.02477592,0.0342636,0.0001091357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937996,0.007292133,0.07235355,0.002196265,0.0002054315,0.0001188454,0.0004099549,0.0002437036,0.02338049],"genre_scores_gemma":[0.9667389,0.002766783,0.02328343,0.00008638857,0.00002532334,0.00006535482,0.000314508,0.00002362557,0.006695583],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007164464,"threshold_uncertainty_score":0.01424557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01056269442097975,"score_gpt":0.2348355824405087,"score_spread":0.2242728880195289,"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."}}