{"id":"W6920611111","doi":"10.60692/rhjpz-da534","title":"Discharge coefficient prediction of canal radial gate using neurocomputing models: an investigation of free and submerged flow scenarios","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Flow (mathematics); Artificial neural network; Sensitivity (control systems); Regression analysis; Correlation coefficient; Discharge coefficient; Flow measurement; Coefficient of determination; Linear regression","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":[0.0004753429,0.0006119894,0.0004802448,0.0004321933,0.0002641568,0.0007879685,0.0006305087,0.0006901898,0.0003500796],"category_scores_gemma":[0.001213813,0.0002278653,0.0005447738,0.0005111208,0.0003588115,0.0006794487,0.0003629672,0.0004991229,0.00006977881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009215539,"about_ca_system_score_gemma":0.001198506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02971834,"about_ca_topic_score_gemma":0.0222719,"domain_scores_codex":[0.9998129,0.00004145901,0.00001083712,0.00005141051,0.00004638365,0.00003703914],"domain_scores_gemma":[0.9995963,0.0002091742,0.00005160163,0.00002296456,0.00009680373,0.00002316765],"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":[0.00004389822,0.00003894281,0.01115589,0.00002378736,0.00002110116,0.00009869727,0.00003377033,0.979735,0.001672306,0.0004221681,0.0001438687,0.006610559],"study_design_scores_gemma":[0.000001936419,0.00001585648,0.001308336,0.000001264528,0.000004100014,0.000004346121,0.0000126013,0.9980707,0.0004346859,0.0001076488,0.00003463588,0.000003884826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714735,0.0001075383,0.02707592,0.0001178954,0.00001452242,0.00001406361,0.0001022531,0.0001357439,0.0009587026],"genre_scores_gemma":[0.997933,0.00004971679,0.00169364,0.00000727663,0.00000213194,0.000006440838,0.00005491073,0.000003985052,0.0002488764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02971834,"threshold_uncertainty_score":0.05909073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02625335980359642,"score_gpt":0.1802709963558747,"score_spread":0.1540176365522783,"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."}}