{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009139691,0.0001163512,0.00009573244,0.0001766388,0.00009439535,0.00004286841,0.00004978216,0.00006184752,0.0000296474],"category_scores_gemma":[0.00001634069,0.0001192197,0.00002029919,0.0001748223,0.00004072122,0.0003694547,0.00001737883,0.00007580868,0.00003242483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004583505,"about_ca_system_score_gemma":0.000003319244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002553924,"about_ca_topic_score_gemma":0.00001713833,"domain_scores_codex":[0.9993865,0.00002233969,0.0001910716,0.0001454247,0.0001120441,0.0001425519],"domain_scores_gemma":[0.9998028,0.0000121864,0.00003230983,0.00008726939,0.00002442399,0.00004098353],"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":[0.0000400994,0.0001192299,0.007582619,0.0002162912,0.00003888814,0.00004861863,0.01215934,0.9236079,0.003723573,0.04209649,0.0005476989,0.009819184],"study_design_scores_gemma":[0.001127713,0.00001932662,0.1301191,0.00004221213,0.000007639164,0.000006352417,0.000187484,0.8612956,0.001432948,0.003613166,0.001892047,0.0002564644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8852859,0.00009795524,0.06148177,0.0001136863,0.0001022148,0.0003115311,0.000003935597,0.0003048134,0.05229819],"genre_scores_gemma":[0.993425,0.00001370981,0.005701585,0.00004224,0.0000345752,0.00002166873,0.0001575253,0.00001901976,0.0005846454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1225365,"threshold_uncertainty_score":0.4861637,"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."}}