{"id":"W2920756880","doi":"10.1016/j.jhydrol.2019.02.027","title":"Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Markov chain; Computer science; Variable (mathematics); Markov model; Markov chain Monte Carlo; Satellite; Reliability (semiconductor); Statistics; Algorithm; Data mining; Monte Carlo method; Mathematics; Machine learning; 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":[0.001508067,0.0004431342,0.0005512336,0.0009533759,0.0003223034,0.0008069925,0.0005026245,0.0004903806,0.001500078],"category_scores_gemma":[0.006613532,0.0005304076,0.0005909976,0.0008869476,0.0003675286,0.001555033,0.0006847897,0.001574255,0.0002061068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000688469,"about_ca_system_score_gemma":0.001259739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01913918,"about_ca_topic_score_gemma":0.02293608,"domain_scores_codex":[0.9996623,0.0001109992,0.00003394031,0.00005926898,0.0001021122,0.00003141568],"domain_scores_gemma":[0.9962932,0.002830041,0.0002189619,0.0001501939,0.0004376463,0.00006982122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005170959,0.00003371295,0.001218342,0.00001537364,0.00005725635,0.00001897102,0.00002094353,0.961675,0.0007158692,0.006424585,0.0004469212,0.02932115],"study_design_scores_gemma":[0.000002280692,0.000003338707,0.00009960357,0.0000012861,0.000003591388,0.000001555597,9.449858e-7,0.9981403,0.0001498923,0.001525347,0.00006997248,0.000001770514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04714609,0.0002457714,0.9508411,0.0001833663,0.00007932596,0.00002157783,0.0001418303,0.0003347649,0.001006174],"genre_scores_gemma":[0.8118141,0.0003370753,0.1849638,0.00009876736,0.0001360514,0.0000899577,0.0004745809,0.00007604832,0.00200966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01913918,"threshold_uncertainty_score":0.03805554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03021617011421007,"score_gpt":0.2974079722248033,"score_spread":0.2671918021105932,"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."}}