{"id":"W2972759951","doi":"10.29103/tj.v9i2.233","title":"STUDI PEMETAAN DAERAH GENANGAN BANJIR DAS SEI KAMBING DENGAN SISTEM INFORMASI GEOGRAFIS","year":2019,"lang":"en","type":"article","venue":"Teras Jurnal Jurnal Teknik Sipil","topic":"Multimedia Learning Systems","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Flood myth; Hydrology (agriculture); Return period; Watershed; Drainage basin; 100-year flood; Environmental science; Flooding (psychology); Flood mitigation; Flood stage; Geography; Geology; Cartography; Archaeology","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002241921,0.0007468676,0.0009794745,0.0006419235,0.000631711,0.001278106,0.003054231,0.0002656707,0.00007034079],"category_scores_gemma":[0.000207935,0.0006547552,0.0005038345,0.001051349,0.0001094314,0.002257967,0.0009263599,0.001591245,0.001571399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003758876,"about_ca_system_score_gemma":0.0003925066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008353942,"about_ca_topic_score_gemma":0.00003197866,"domain_scores_codex":[0.9936358,0.0005166255,0.001483432,0.001021832,0.001918581,0.001423697],"domain_scores_gemma":[0.9958579,0.0004858234,0.0009314895,0.001603798,0.000420221,0.0007007711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002101541,0.000937191,0.6155676,0.0009260153,0.001797512,0.001151847,0.02981657,0.003142809,0.07015967,0.01050914,0.005868524,0.259913],"study_design_scores_gemma":[0.007859968,0.001685732,0.3492399,0.001500362,0.0002506831,0.00335961,0.00128262,0.0754988,0.01270635,0.0002138356,0.5425481,0.003854064],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9775463,0.000856804,0.007463222,0.0007079117,0.003790206,0.0008431962,0.000005044221,0.0008002888,0.007987056],"genre_scores_gemma":[0.984455,0.00005083462,0.006625653,0.0008832597,0.0009470428,0.00003309162,0.00001346321,0.00009320176,0.006898403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5366796,"threshold_uncertainty_score":0.9997587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133722944,"score_gpt":0.2529059577745279,"score_spread":0.2415687283345279,"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."}}