{"id":"W3035853736","doi":"10.5539/jas.v12n7p135","title":"Spate Irrigation Potential Assessment for Ethiopian Watershed","year":2020,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Irrigation; Environmental science; Irrigation statistics; Hydrology (agriculture); Water resource management; Watershed; Irrigation district; Deficit irrigation; Evapotranspiration; Irrigation management; Surface irrigation; Flood myth; Geography; Agronomy; Ecology; Geology; 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":[0.0002632306,0.0002037614,0.0001529492,0.001164863,0.0002672595,0.0007826433,0.000141307,0.0002161655,0.001064765],"category_scores_gemma":[0.0004620093,0.0001272525,0.0002379749,0.001048342,0.0001527662,0.0004178679,0.0004496882,0.0001089578,0.0001198371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004359916,"about_ca_system_score_gemma":0.0003834009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00276335,"about_ca_topic_score_gemma":0.007341506,"domain_scores_codex":[0.9998879,0.00002923246,0.000009079695,0.0000153475,0.0000305568,0.00002787862],"domain_scores_gemma":[0.9998164,0.00005028629,0.00004057537,0.000009638075,0.00005615407,0.00002707371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002488577,0.0001407927,0.8431277,0.0001982379,0.00009944081,0.002164356,0.001228373,0.0726316,0.02382073,0.002144633,0.0007916226,0.05340368],"study_design_scores_gemma":[0.00001964183,0.000354503,0.844161,0.00007283168,0.00007908051,0.000867996,0.007376885,0.1306337,0.008191456,0.002078242,0.006110516,0.00005412046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970969,0.00005885424,0.001223637,0.00001857352,0.000001095533,0.00001528046,0.0003376776,0.00001275144,0.001235338],"genre_scores_gemma":[0.9986265,0.00005727665,0.0008570049,0.000002591349,8.221231e-7,0.00001105393,0.0001867264,0.000001815801,0.0002563111],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00276335,"threshold_uncertainty_score":0.005494475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285145414260283,"score_gpt":0.2416336251072775,"score_spread":0.2287821709646747,"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."}}