{"id":"W2746934195","doi":"10.1007/s11356-017-9955-8","title":"Optimum ridge-to-furrow ratio in ridge-furrow mulching systems for improving water conservation in maize (Zea may L.) production","year":2017,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Irrigation Practices and Water Management","field":"Agricultural and Biological Sciences","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China","keywords":"Ridge; Sowing; Water-use efficiency; Mulch; Arid; Agronomy; Rainwater harvesting; Evapotranspiration; Environmental science; Water content; Precipitation; Moisture; Biology; Irrigation; Geology; Geography","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.005231577,0.00009403811,0.00009837373,0.0001030279,0.00151452,0.0006223136,0.0003304158,0.00004778656,0.00003375599],"category_scores_gemma":[0.0002087804,0.00004391253,0.00001702875,0.000200598,0.0003445546,0.001315157,0.0002935041,0.0001512929,0.00003393402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000265414,"about_ca_system_score_gemma":0.0000140842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002981851,"about_ca_topic_score_gemma":0.0005252224,"domain_scores_codex":[0.9980806,0.0001279462,0.0002230457,0.0004824161,0.0005929496,0.0004930065],"domain_scores_gemma":[0.999631,0.00004041391,0.00007547306,0.0001219974,0.00003140493,0.00009966428],"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":[0.00007336817,0.00008585812,0.02816353,0.00002060272,0.000001594025,0.000002758372,0.0005630156,0.000442431,0.9478297,0.0002875542,0.000217113,0.02231247],"study_design_scores_gemma":[0.0004280288,0.0002941028,0.9242097,0.00004866364,0.000003421424,0.000004992112,0.002584285,0.01327104,0.03988244,0.0002076603,0.0188304,0.0002352307],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.979327,0.00003043576,0.00001822496,0.0191216,0.0002280747,0.001051826,0.00002038631,0.000008501564,0.0001939518],"genre_scores_gemma":[0.9985397,0.00004209146,0.00007247986,0.0001355326,0.0001257703,0.0001428399,0.00002420574,0.000001225453,0.0009161317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9079472,"threshold_uncertainty_score":0.9997854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06146861389194047,"score_gpt":0.3156672520121495,"score_spread":0.254198638120209,"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."}}