{"id":"W4225083972","doi":"10.48550/arxiv.2204.12694","title":"Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","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":"","keywords":"Computer science; Robustness (evolution); Artificial neural network; Model predictive control; Benchmark (surveying); Offset (computer science); DNS root zone; Control theory (sociology); Tracking error; Control (management); Artificial intelligence; Environmental science; Soil science; Soil water","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":[0.0004927993,0.000301901,0.0004328657,0.00003807939,0.0002904481,0.00007010033,0.0006899442,0.0002560696,0.0002355659],"category_scores_gemma":[0.00003236494,0.0001553803,0.000279408,0.0003411995,0.00006019806,0.0001258218,0.0005672015,0.0008183617,0.000006972859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001184074,"about_ca_system_score_gemma":0.00002321409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003007035,"about_ca_topic_score_gemma":0.00001451243,"domain_scores_codex":[0.9978374,0.000438005,0.0003112428,0.0008256744,0.0002152759,0.0003724338],"domain_scores_gemma":[0.9987271,0.0003655833,0.0004366529,0.0002393788,0.0001127989,0.0001184386],"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":[0.0005243692,0.0001405378,0.00197894,0.00002387303,0.00007580617,0.00003744849,0.00002524862,0.9809849,0.00004209406,0.01605396,0.000057729,0.00005511721],"study_design_scores_gemma":[0.0003559701,0.0003408572,0.0003038181,0.00006330069,0.0001625688,3.693045e-7,0.0001602989,0.9825085,0.000001487969,0.01573601,0.00009729103,0.0002695377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8400837,0.00005349782,0.1554168,0.0004322011,0.0005162118,0.0008372001,0.0001809294,0.0001307341,0.002348669],"genre_scores_gemma":[0.9987924,0.00002557271,0.00009016052,0.0005561138,0.0002195983,0.00001110304,0.0001022459,0.000003146547,0.0001996899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1587087,"threshold_uncertainty_score":0.6336224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09792550270523,"score_gpt":0.2003924182250272,"score_spread":0.1024669155197972,"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."}}