{"id":"W2084886995","doi":"10.1002/ird.381","title":"Optimal cultivation rules in multi‐crop irrigation areas","year":2008,"lang":"en","type":"article","venue":"Irrigation and Drainage","topic":"Water resources management and optimization","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Irrigation; Time horizon; Inflow; Linear programming; Agricultural engineering; Robustness (evolution); Present value; Computer science; Water resource management; Operations research; Environmental science; Mathematics; Mathematical optimization; Engineering; Geography; Business; Meteorology","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":[0.00106516,0.0003837338,0.0008723069,0.0003805516,0.0003566686,0.00139315,0.0009284968,0.0007598852,0.001453565],"category_scores_gemma":[0.001910571,0.0005066808,0.0005922435,0.0005094672,0.0006848733,0.001034867,0.0004734667,0.0006699641,0.0001498681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169048,"about_ca_system_score_gemma":0.001168189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00834968,"about_ca_topic_score_gemma":0.005844214,"domain_scores_codex":[0.9994432,0.0001884983,0.00002922899,0.000149381,0.0000826547,0.0001070156],"domain_scores_gemma":[0.9989048,0.0006190451,0.0002408346,0.00004974854,0.0001184984,0.00006711069],"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":[0.00000969686,0.000008417815,0.0002823769,0.000006476464,0.000004272247,0.00002499888,0.000009299578,0.9969038,0.0002309545,0.0008244304,0.00006006181,0.001635185],"study_design_scores_gemma":[0.000004368922,0.00001031318,0.000140368,0.000002815171,0.000003009387,0.00000650329,0.00001015563,0.9982827,0.0002163324,0.0011746,0.0001461716,0.000002676765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4694161,0.0003746277,0.5195608,0.0003850701,0.00003040996,0.0001221781,0.0004139043,0.0002618985,0.0094351],"genre_scores_gemma":[0.9748638,0.0000837948,0.02357297,0.0000211061,0.000005065743,0.00005104187,0.00008933489,0.00001964344,0.001293158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00834968,"threshold_uncertainty_score":0.01660216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917087043257878,"score_gpt":0.2145023566072151,"score_spread":0.1953314861746363,"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."}}