{"id":"W3049418060","doi":"10.5539/jas.v12n9p34","title":"Irrigation Scheduling to Promote Corn Productivity in Central Alabama","year":2020,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Auburn University","keywords":"Irrigation; Irrigation scheduling; Evapotranspiration; Environmental science; Water content; Agronomy; DNS root zone; Growing season; Soil water; Field experiment; Deficit irrigation; Irrigation management; Hydrology (agriculture); Soil science; Ecology; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003595506,0.000188578,0.0001409747,0.0002924585,0.0004481048,0.0005104262,0.0004999664,0.0001280006,0.0006543469],"category_scores_gemma":[0.0004339178,0.0001074164,0.0001267606,0.0004231082,0.0001979931,0.0001949549,0.0003258639,0.0002011428,0.00004563399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002406945,"about_ca_system_score_gemma":0.002370134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07300685,"about_ca_topic_score_gemma":0.2417385,"domain_scores_codex":[0.9998485,0.00004815531,0.000008926853,0.00003774891,0.00003167154,0.00002490657],"domain_scores_gemma":[0.9996269,0.00007033326,0.0001111806,0.00002243901,0.00009134052,0.00007776404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.003060771,0.002391677,0.2020645,0.0003279217,0.0001306441,0.0006378395,0.001341095,0.008228776,0.6767406,0.0009241296,0.0008814656,0.1032705],"study_design_scores_gemma":[0.0002093488,0.003317948,0.9078193,0.00005396999,0.0001379717,0.0001528018,0.003437768,0.02060349,0.05478882,0.0003758882,0.009055217,0.00004750219],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985393,0.00008048992,0.0005247347,0.00006283908,0.000004265371,0.00002664278,0.00003208313,0.00001992003,0.0007096961],"genre_scores_gemma":[0.9959143,0.0001485666,0.002885113,0.00005384156,0.000002104451,0.00003980255,0.00007758509,0.000005081877,0.0008735027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07300685,"threshold_uncertainty_score":0.1451638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639218769143437,"score_gpt":0.2375911318315666,"score_spread":0.2111989441401323,"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."}}