{"id":"W3036992454","doi":"","title":"Incorporating sharp-crested weirs into irrigation SCADA systems","year":2020,"lang":"en","type":"article","venue":"Digital Collections of Colorado (Colorado State University)","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SCADA; Irrigation; Hydrology (agriculture); Environmental science; Water resource management; Engineering; Civil engineering; Computer science; Petroleum engineering; Geotechnical engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00076858,0.0004006527,0.0003624524,0.0003619736,0.0004998327,0.00118214,0.0007783811,0.0004184277,0.003190012],"category_scores_gemma":[0.001889059,0.000308366,0.0003402965,0.0004429695,0.000348201,0.0009648748,0.001043425,0.0008600209,0.0005513392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004633962,"about_ca_system_score_gemma":0.0008142231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007667471,"about_ca_topic_score_gemma":0.02729206,"domain_scores_codex":[0.9994422,0.0002003232,0.00004604697,0.0001042365,0.0001503276,0.00005688027],"domain_scores_gemma":[0.9989702,0.0001982627,0.0001162443,0.0002673788,0.0003537679,0.0000942167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003174768,0.0004067713,0.01675471,0.0001744534,0.0001645087,0.0004911764,0.0004799029,0.4683519,0.03108699,0.01503734,0.006154751,0.4605799],"study_design_scores_gemma":[0.0001390393,0.001441733,0.009435439,0.00006453085,0.0002819522,0.0001932789,0.0003513564,0.9289724,0.01267387,0.005591619,0.04075293,0.0001018795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2602349,0.0005158127,0.707743,0.000854572,0.000386707,0.0004324742,0.000242621,0.004399323,0.02519061],"genre_scores_gemma":[0.9167024,0.0001713914,0.07854401,0.00005625062,0.00003633015,0.00003756287,0.00007016859,0.00007151407,0.004310459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007667471,"threshold_uncertainty_score":0.01524568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775041335333252,"score_gpt":0.1748479010818274,"score_spread":0.1570974877284948,"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."}}