{"id":"W2996139122","doi":"10.3390/w12010005","title":"Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning","year":2019,"lang":"en","type":"article","venue":"Water","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"","keywords":"Hydrology (agriculture); Environmental science; Evapotranspiration; Baseflow; Groundwater; Multilayer perceptron; Streamflow; Watershed; Artificial neural network; Geography; Machine learning; Drainage basin; Geology; Computer science; Cartography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003600461,0.0005160292,0.0002980374,0.001039081,0.0002303393,0.0005981232,0.0004155916,0.0004155893,0.0003581099],"category_scores_gemma":[0.001099142,0.0003007459,0.0003634799,0.001276376,0.0002182581,0.0007565795,0.0005409951,0.0004813597,0.00009514562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009858881,"about_ca_system_score_gemma":0.0008949041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05169048,"about_ca_topic_score_gemma":0.08519846,"domain_scores_codex":[0.9998361,0.00002738346,0.0000160906,0.00004581137,0.00005421745,0.00002032084],"domain_scores_gemma":[0.9997205,0.00008709177,0.00005746843,0.00002391301,0.00009644695,0.00001458584],"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":[0.00009920164,0.0001246077,0.1044964,0.0001714633,0.000234511,0.000194092,0.0001395733,0.636564,0.0162623,0.001464488,0.001200974,0.2390484],"study_design_scores_gemma":[0.000006391808,0.00001868394,0.01905813,0.00001228747,0.0000175036,0.00001618045,0.0000445506,0.9757474,0.003672878,0.0008722206,0.0005177489,0.00001597569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7013541,0.0009944024,0.2917463,0.0003424651,0.00005301322,0.00005353423,0.001426415,0.001139928,0.002889744],"genre_scores_gemma":[0.9574493,0.000223337,0.04078853,0.000033835,0.00001229234,0.00002126252,0.0007145251,0.00002171576,0.0007351943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05169048,"threshold_uncertainty_score":0.1027792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732862895662417,"score_gpt":0.2256427347592902,"score_spread":0.208314105802666,"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."}}