{"id":"W2765262425","doi":"10.1016/j.compag.2017.10.023","title":"Development of an irrigation scheduling software based on model predicted crop water stress","year":2017,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":85,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Ten Thousand Talent Program; China Scholarship Council; McGill University","keywords":"Irrigation; Irrigation scheduling; Environmental science; Deficit irrigation; Water content; Hydrology (agriculture); Soil water; Irrigation management; Low-flow irrigation systems; Crop coefficient; Field capacity; Agronomy; Soil science; Engineering; Geotechnical engineering","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":[0.0006329287,0.0009321141,0.0007551922,0.0006602313,0.0004146407,0.0006588902,0.001704406,0.0004465537,0.007279157],"category_scores_gemma":[0.002016715,0.0006523544,0.0006914384,0.0003682927,0.0002643691,0.0006110123,0.0004149826,0.0009803617,0.001685921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005725116,"about_ca_system_score_gemma":0.001451524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007901473,"about_ca_topic_score_gemma":0.005073185,"domain_scores_codex":[0.9997426,0.00003354419,0.00002255533,0.00009844136,0.00007666818,0.0000262916],"domain_scores_gemma":[0.9989399,0.0003571663,0.00008626029,0.0001241831,0.0004090847,0.00008342427],"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":[0.0009139266,0.0008830036,0.01538975,0.0005287051,0.0004870967,0.0006630025,0.0004820843,0.4647628,0.1142888,0.006570317,0.02889946,0.366131],"study_design_scores_gemma":[0.0001371371,0.00008357193,0.001627268,0.00001819639,0.00007027738,0.00006947876,0.00001957155,0.9649292,0.02502698,0.0009718523,0.007010588,0.00003601099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04621441,0.00009058542,0.8505918,0.0001229461,0.000193192,0.0003752965,0.0007466207,0.09740252,0.004262594],"genre_scores_gemma":[0.5249552,0.0002163946,0.4548453,0.0002524834,0.0000848948,0.0006450464,0.002725232,0.006807092,0.009468397],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007901473,"threshold_uncertainty_score":0.02435118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723081669024019,"score_gpt":0.2284418281133934,"score_spread":0.2112110114231532,"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."}}