{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000398287,0.0002236513,0.0002923229,0.0001442802,0.0004088885,0.0004462945,0.001066957,0.00007628045,0.00003850934],"category_scores_gemma":[0.0003564956,0.000210387,0.0001563219,0.0009014211,0.00006589163,0.0004360408,0.0006172155,0.0004645854,0.0002142706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006118869,"about_ca_system_score_gemma":0.0001893789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001111178,"about_ca_topic_score_gemma":0.00006539853,"domain_scores_codex":[0.997998,0.0001508953,0.0003542538,0.0006729304,0.0004051902,0.0004187398],"domain_scores_gemma":[0.9979593,0.0001589857,0.0001560476,0.001216558,0.0002702778,0.0002388601],"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":[0.00003218879,0.001116078,0.03559844,0.00007262825,0.0006120983,0.0007055353,0.0077082,0.0003222603,0.01803649,0.1177321,0.3651107,0.4529532],"study_design_scores_gemma":[0.001331178,0.0003361042,0.1081992,0.000159958,0.0001610399,0.0008698524,0.002439671,0.01722365,0.006019759,0.0018168,0.8602985,0.001144246],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6740839,0.00340708,0.1797698,0.07800005,0.002272735,0.0004442379,0.0001409162,0.002071948,0.0598094],"genre_scores_gemma":[0.9797126,0.0000819432,0.01583474,0.001845202,0.0003536934,0.00002548622,0.00009923876,0.00002809358,0.002018984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4951878,"threshold_uncertainty_score":0.8579333,"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."}}