{"id":"W2807567822","doi":"10.5539/jas.v10n7p55","title":"Optimizing Irrigation Depth Using a Plant Growth Model and Weather Forecast","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Irrigation; Arachis hypogaea; Agricultural engineering; Environmental science; Center pivot irrigation; Irrigation scheduling; Irrigation management; Surface irrigation; Net income; Deficit irrigation; Yield (engineering); Hydrology (agriculture); Water resource management; Agronomy; Geology; Engineering; Economics; Physics","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":[0.000201599,0.0003939637,0.0003544987,0.0001669188,0.0001609564,0.0003156965,0.0004152352,0.0005643374,0.0004772832],"category_scores_gemma":[0.000562958,0.0002309179,0.0003134186,0.0002024297,0.0002343145,0.0004410433,0.0003466647,0.000301243,0.00006683254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004352188,"about_ca_system_score_gemma":0.0007237818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007658652,"about_ca_topic_score_gemma":0.00797243,"domain_scores_codex":[0.999922,0.00002042397,0.000004679307,0.00002317089,0.00001749533,0.00001213147],"domain_scores_gemma":[0.9998299,0.00007670165,0.00003852438,0.00001470154,0.00002818633,0.00001209237],"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":[0.000009696952,0.00001223129,0.000611829,0.000007301551,0.000004256378,0.000009030006,0.000007331261,0.9934302,0.002865918,0.0002561282,0.0000250104,0.002761115],"study_design_scores_gemma":[0.000004631744,0.00001065742,0.0001690542,5.739972e-7,0.000002196197,0.000001920792,0.000001992869,0.9993464,0.0003131793,0.00009541478,0.00005221098,0.000001715457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4556873,0.0001214597,0.5399965,0.0001069292,0.00002108852,0.00008003097,0.0001111255,0.0002594196,0.00361619],"genre_scores_gemma":[0.9608788,0.00005120162,0.03833029,0.00001169365,0.000004638556,0.00005030428,0.00003824299,0.00001513318,0.0006197417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007658652,"threshold_uncertainty_score":0.01522815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04214071328371184,"score_gpt":0.2458746018181479,"score_spread":0.2037338885344361,"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."}}