{"id":"W3198215115","doi":"10.5614/jts.2021.28.2.5","title":"Analisis Tren Perubahan Intensitas Hujan (Studi Kasus: Jakarta dan Bogor)","year":2021,"lang":"en","type":"article","venue":"Jurnal Teknik Sipil","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Flood myth; Indonesian; Drainage; Environmental science; Intensity (physics); Hydrology (agriculture); Meteorology; Geography; Statistics; Mathematics; Engineering; Geotechnical engineering","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":[0.0003052735,0.0002935648,0.0002497249,0.002413949,0.0003341954,0.001244662,0.0001772263,0.0001263695,0.004478916],"category_scores_gemma":[0.0006241638,0.0001327243,0.000193606,0.002992467,0.0002493371,0.0003157679,0.0006747202,0.0002337718,0.0005514385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007164518,"about_ca_system_score_gemma":0.0006752657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03631977,"about_ca_topic_score_gemma":0.06360988,"domain_scores_codex":[0.9997861,0.00004622899,0.00002393694,0.00003956845,0.0000628478,0.0000414198],"domain_scores_gemma":[0.9996685,0.0001108808,0.00006351903,0.00002259788,0.0001049862,0.00002957424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001454952,0.0000755414,0.8769931,0.0005261787,0.0002427253,0.002061503,0.008438291,0.001800294,0.00451324,0.002733133,0.004600911,0.09786956],"study_design_scores_gemma":[0.000001565019,0.00001223467,0.9803967,0.00005942188,0.00003329459,0.0003027582,0.006496336,0.0005714064,0.0005931479,0.000108207,0.01141576,0.000009120639],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784798,0.0009651529,0.0005033379,0.00008038183,0.00001142848,0.00003399912,0.003026548,0.00007054503,0.01682876],"genre_scores_gemma":[0.9920929,0.0006131573,0.0006818466,0.00001613642,0.000006144844,0.00004398666,0.00223695,0.00002320876,0.004285659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03631977,"threshold_uncertainty_score":0.07221669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479303300369276,"score_gpt":0.2647097407335559,"score_spread":0.2499167077298631,"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."}}