{"id":"W4296908678","doi":"10.1109/adconip55568.2022.9894137","title":"A two-layer NN framework for modeling agro-hydrological systems","year":2022,"lang":"en","type":"article","venue":"","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Artificial neural network; Layer (electronics); Water resources; Nonlinear system; System dynamics; Mathematical optimization; Artificial intelligence; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000371664,0.0006949115,0.0005178195,0.0002674588,0.0002877671,0.0006594945,0.001076483,0.001081123,0.001977838],"category_scores_gemma":[0.0005666388,0.000317725,0.0005542305,0.0004047956,0.0002398046,0.0008164358,0.0006075464,0.0009066668,0.0003803222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005752312,"about_ca_system_score_gemma":0.000794068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01897696,"about_ca_topic_score_gemma":0.01902604,"domain_scores_codex":[0.9998789,0.00002480696,0.00001003403,0.00004159529,0.00002487265,0.00001976721],"domain_scores_gemma":[0.9998912,0.00003610668,0.00001346804,0.000007667083,0.00004375613,0.000007861518],"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":[0.0000155966,0.00001426207,0.0002531497,0.00003685384,0.00002638682,0.00002759267,0.00001263836,0.9817632,0.001078945,0.002907288,0.0003467545,0.01351721],"study_design_scores_gemma":[0.000001030753,0.000003914139,0.00004892583,0.000001481566,0.000002478666,0.000002602298,0.000001078369,0.9989346,0.0000873719,0.0007503023,0.0001645942,0.000001785464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0172313,0.0009021951,0.9768831,0.0002016165,0.0001039928,0.00003242952,0.0002693461,0.0005428931,0.003833106],"genre_scores_gemma":[0.8310093,0.001089723,0.1575509,0.0001824019,0.0001212611,0.0002679309,0.0005823435,0.00007285433,0.009123348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01897696,"threshold_uncertainty_score":0.03773302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07257033410573847,"score_gpt":0.277583101358322,"score_spread":0.2050127672525835,"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."}}