{"id":"W4384208855","doi":"10.20885/teknisia.vol28.iss1.art5","title":"KAJIAN PERUBAHAN IKLIM DI DKI JAKARTA BERDASARKAN DATA CURAH HUJAN","year":2023,"lang":"en","type":"article","venue":"Teknisia","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downscaling; Environmental science; Flooding (psychology); Climatology; Climate change; Monsoon; Wet season; Global warming; Climate model; Meteorology; Geography; Precipitation; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003704622,0.000544503,0.0004415188,0.001499925,0.001304727,0.002292562,0.0004699306,0.0003178404,0.06991563],"category_scores_gemma":[0.001069931,0.0002842495,0.0002647654,0.002747205,0.0003248354,0.0008252253,0.001042349,0.0007208016,0.01444048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001745715,"about_ca_system_score_gemma":0.004000688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06115334,"about_ca_topic_score_gemma":0.09655327,"domain_scores_codex":[0.9997358,0.00003617748,0.00002346624,0.00005215532,0.00008664318,0.00006575756],"domain_scores_gemma":[0.9993581,0.00008644426,0.00007313076,0.00008257879,0.0003144465,0.00008531062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0010723,0.0002644114,0.09588148,0.00250773,0.0002190288,0.003041451,0.003234918,0.002791682,0.006387734,0.02332473,0.5203751,0.3408994],"study_design_scores_gemma":[0.0000369178,0.00003236739,0.1180306,0.0003494755,0.00007425791,0.0005909214,0.002746756,0.001294528,0.002604087,0.001250664,0.8729336,0.00005585921],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1922706,0.01317643,0.005619523,0.006386361,0.001540831,0.0005186282,0.1640184,0.00297279,0.6134966],"genre_scores_gemma":[0.4589284,0.01210496,0.01146778,0.0009798573,0.0003187894,0.0006481442,0.106648,0.0009887064,0.4079154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06991563,"threshold_uncertainty_score":0.2338911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06478516333902466,"score_gpt":0.3331030571871688,"score_spread":0.2683178938481441,"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."}}