{"id":"W2981717355","doi":"10.3390/agronomy9110686","title":"A Model-Based Real-Time Decision Support System for Irrigation Scheduling to Improve Water Productivity","year":2019,"lang":"en","type":"article","venue":"Agronomy","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Irrigation scheduling; Irrigation; Environmental science; Low-flow irrigation systems; Deficit irrigation; Agricultural engineering; Water content; Irrigation management; Arid; Scheduling (production processes); Soil moisture sensor; Hydrology (agriculture); Water resource management; Soil water; Soil science; Agronomy; Engineering; Operations management; Geology","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":[0.0005396029,0.0001080679,0.0001297496,0.00001552973,0.0001276878,0.0001080165,0.0001368491,0.00004497291,0.0001443707],"category_scores_gemma":[0.000009765688,0.0000389573,0.00006718253,0.00007424301,0.000007051954,0.0003303129,0.0000583517,0.00003539385,0.0006328141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006329923,"about_ca_system_score_gemma":0.000009007082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009608759,"about_ca_topic_score_gemma":0.00001468058,"domain_scores_codex":[0.999032,0.00002505014,0.0001913061,0.0003787395,0.0001390846,0.0002338399],"domain_scores_gemma":[0.9996027,0.00006781093,0.00007241581,0.00009703964,0.00009221296,0.00006777726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003024448,0.0001060686,0.001087766,0.00009431821,0.00002509708,7.036662e-7,0.0001851881,0.01757053,0.8874339,0.001683347,0.0005597324,0.09095084],"study_design_scores_gemma":[0.002011998,0.001959263,0.01255549,0.0002007302,0.0001436394,0.000002643241,0.0007312291,0.3844323,0.4921712,0.004226798,0.1001892,0.001375542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904601,0.000001078033,0.003335275,0.001700343,0.0001663213,0.001285248,0.00001227846,0.00007344708,0.0029659],"genre_scores_gemma":[0.9884655,1.518821e-7,0.009039084,0.0001206802,0.000125219,0.0001496674,0.0001366115,0.000001631966,0.001961481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3952627,"threshold_uncertainty_score":0.8133757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603848708375465,"score_gpt":0.2288792905984135,"score_spread":0.2128408035146588,"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."}}