{"id":"W4313447909","doi":"10.21203/rs.3.rs-2379549/v1","title":"Operational, Economic and Environmental Advantages of Applying Artificial Intelligence in Dam Operations: an approach based on artificial neural networks and Monte Carlo simulation method for floodgate operation","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Water resources management and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Monte Carlo method; Artificial neural network; Computer science; Multilayer perceptron; Artificial intelligence; Perceptron; Machine learning; Operations research; Engineering; Mathematics","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":[0.001777171,0.0003159341,0.0003747726,0.001116635,0.0002742376,0.0009649437,0.0005214461,0.0006475155,0.001183277],"category_scores_gemma":[0.005129945,0.0002456082,0.0004710174,0.0009408689,0.0004086564,0.000744413,0.0004666772,0.0004913426,0.00006299382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008376461,"about_ca_system_score_gemma":0.001201508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005717909,"about_ca_topic_score_gemma":0.006849151,"domain_scores_codex":[0.9991336,0.0006146188,0.00004013279,0.00005657861,0.0001287503,0.0000262533],"domain_scores_gemma":[0.9970203,0.002402422,0.0001881362,0.00008859008,0.0002640693,0.00003645388],"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":[0.00004689946,0.00006277182,0.002884926,0.0001361573,0.00009353759,0.00006094919,0.00006345334,0.9602804,0.0005863174,0.01086508,0.0002090898,0.02471043],"study_design_scores_gemma":[0.000004220741,0.00001909968,0.0003992904,0.00001829333,0.00001560012,0.000007572523,0.00001851081,0.996854,0.000201232,0.002222121,0.0002352853,0.000004792643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1838625,0.001082392,0.8010942,0.0008180851,0.00009007852,0.0002230771,0.0001472552,0.0001789027,0.01250352],"genre_scores_gemma":[0.8738331,0.0004468453,0.1244618,0.00004847211,0.00002160085,0.0001894174,0.00003987519,0.0000146314,0.0009442536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005717909,"threshold_uncertainty_score":0.01136923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06383538034278453,"score_gpt":0.3555866190410368,"score_spread":0.2917512386982523,"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."}}