{"id":"W4327518168","doi":"10.1007/978-3-030-89123-7_266-1","title":"Model Predictive Control for Irrigation Scheduling","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Model predictive control; Irrigation scheduling; Computer science; Scheduling (production processes); Irrigation; Environmental science; Water resource management; Control (management); Operations management; Economics; Artificial intelligence; Agronomy; Biology","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":[0.0002231043,0.0001613539,0.0001667775,0.00001379998,0.0001568723,0.00007886527,0.0001315078,0.0001666319,0.0002948928],"category_scores_gemma":[0.00001787861,0.00005828391,0.0001340033,0.00001960936,0.0000200684,0.000141556,0.00003985867,0.0001002907,0.0001810233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002624723,"about_ca_system_score_gemma":0.000006222344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001577728,"about_ca_topic_score_gemma":0.00007371067,"domain_scores_codex":[0.9991345,0.00000628372,0.0002182919,0.0002994499,0.0001879278,0.0001535673],"domain_scores_gemma":[0.9994298,0.0001729915,0.0001836499,0.00004169574,0.0001240262,0.00004781788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007739924,0.0000116714,0.000003675633,0.00002442654,0.0001219302,0.0000010718,0.00002486795,0.007055478,0.0006118633,0.9697624,0.003323724,0.01898145],"study_design_scores_gemma":[0.0002808619,0.0002247341,0.00006616519,0.00005451894,0.0001458541,3.406349e-7,0.00005311335,0.358254,0.00003107549,0.5437996,0.09677535,0.0003144266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001671517,0.00002787621,0.01636639,0.005887241,0.0003573999,0.001652106,0.0005278735,0.0003943959,0.9746196],"genre_scores_gemma":[0.05061574,0.0000330201,0.0006823678,0.00071806,0.0004299813,0.00009547461,0.0006066666,0.000003715351,0.946815],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4259629,"threshold_uncertainty_score":0.3228869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05896055331664059,"score_gpt":0.2438474563085792,"score_spread":0.1848869029919386,"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."}}