{"id":"W2053934894","doi":"10.2166/hydro.2012.130","title":"Improving reliability of river flow forecasting using neural networks, wavelets and self-organising maps","year":2012,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Reliability (semiconductor); Artificial neural network; Computer science; Wavelet; Cluster (spacecraft); Artificial intelligence; Data mining; Machine learning; Pattern recognition (psychology)","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":[0.001856282,0.000174728,0.0003431615,0.00006007358,0.0001718221,0.00003846543,0.0001936727,0.0001025897,0.00004583128],"category_scores_gemma":[0.0005508974,0.0001353826,0.00009705751,0.0002136369,0.0002113848,0.001343127,0.000287575,0.0003728498,0.000003209749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002036095,"about_ca_system_score_gemma":0.00001457963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002946801,"about_ca_topic_score_gemma":0.000001069372,"domain_scores_codex":[0.9979698,0.00007281505,0.00099093,0.00008257423,0.0004266876,0.000457187],"domain_scores_gemma":[0.9982868,0.0002003486,0.001090461,0.0001650291,0.00003213737,0.0002251908],"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":[0.00002252096,0.0001066785,0.05945421,0.00008755932,0.00001722824,0.000008046591,0.002784312,0.9274036,0.0009218075,0.000002977713,0.00006357882,0.009127438],"study_design_scores_gemma":[0.0002681258,0.0001446013,0.003403544,0.00005307113,0.00006738665,0.0009235728,0.00005334865,0.9944514,0.0003195186,0.0001122602,0.00006147832,0.0001416819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927997,0.00005030314,0.006496981,0.00002419391,0.0002682288,0.00009229429,0.000001891245,0.00001772282,0.0002486757],"genre_scores_gemma":[0.833193,0.000005895754,0.1666066,0.00006428513,0.0001142943,1.227288e-7,6.007033e-7,0.00001274401,0.000002375747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1601097,"threshold_uncertainty_score":0.5520741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698085648674403,"score_gpt":0.2152349415636803,"score_spread":0.1982540850769363,"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."}}