{"id":"W2623181128","doi":"10.1016/j.jhydrol.2017.06.004","title":"Bayesian flood forecasting methods: A review","year":2017,"lang":"en","type":"review","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":158,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Flood forecasting; Flood myth; Bayesian probability; Probabilistic forecasting; Computer science; Probabilistic logic; Bayesian inference; Machine learning; Artificial intelligence; Geography","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003405083,0.0003866151,0.002900801,0.0001459525,0.0002750582,0.00001785579,0.001103026,0.0002878656,0.001090277],"category_scores_gemma":[0.0006672263,0.0002571638,0.0008302898,0.0001155047,0.0003496753,0.0001958268,0.0006858097,0.0008087169,0.0002744718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037328,"about_ca_system_score_gemma":0.0000332641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008646278,"about_ca_topic_score_gemma":0.000009565196,"domain_scores_codex":[0.9968763,0.0009139242,0.001248416,0.0003173254,0.0002076245,0.0004364043],"domain_scores_gemma":[0.9963703,0.0002931552,0.002710835,0.0004963555,0.00001476197,0.0001145545],"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":[0.000004633269,0.00003208286,0.00003139659,0.005894364,0.0004354204,0.0003621971,0.00002630554,0.00001184012,8.677315e-8,0.000005977038,0.01457467,0.978621],"study_design_scores_gemma":[0.0001348811,0.0002004025,0.000002599679,0.008818747,0.002465951,0.001641611,0.000001214619,0.00003100529,1.268695e-7,0.0005842021,0.9859142,0.0002050351],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.470824e-7,0.9859176,0.00115396,0.0007191811,0.000653349,0.0003420292,0.00000178997,0.000009197192,0.01120207],"genre_scores_gemma":[0.000005330836,0.9927875,0.005600995,0.0007177742,0.0002352327,0.00001998198,0.000002642879,0.00002981283,0.0006007549],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.978416,"threshold_uncertainty_score":0.9999881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1371037148498579,"score_gpt":0.4115391390905478,"score_spread":0.2744354242406899,"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."}}