{"id":"W2908258567","doi":"10.1016/j.hydroa.2018.100015","title":"Comparing hydrological frameworks for simulating crop biomass, water and nitrogen dynamics in a tile drained soybean-corn system: Cascade vs computational approach","year":2018,"lang":"en","type":"article","venue":"Journal of Hydrology X","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Agriculture and Agri-Food Canada","funders":"","keywords":"Environmental science; Tile drainage; Biomass (ecology); Agronomy; Hydrology (agriculture); Soil water; Soil science; Geology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006173445,0.0006563156,0.0005469791,0.0005085941,0.0004328132,0.0006865119,0.001124828,0.0009399407,0.00125651],"category_scores_gemma":[0.00138471,0.0004051331,0.0007223492,0.0003851609,0.0005481675,0.0006523522,0.0008273153,0.0006512881,0.00009684019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001667766,"about_ca_system_score_gemma":0.001517526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0721269,"about_ca_topic_score_gemma":0.0443438,"domain_scores_codex":[0.9998572,0.00004785613,0.00001123047,0.00003361992,0.000020527,0.00002959619],"domain_scores_gemma":[0.999396,0.0003271422,0.00006718083,0.00003880168,0.00009468588,0.00007618113],"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":[0.00005747151,0.00007321683,0.003262594,0.00001439818,0.00001330782,0.00002977181,0.00002014454,0.9937331,0.0005804032,0.0004836096,0.0000503054,0.001681615],"study_design_scores_gemma":[0.00001506427,0.00002014854,0.0004008974,0.000001552373,0.000003595057,0.00000202925,0.00001291493,0.9992422,0.0001446514,0.0001131845,0.00004139561,0.000002454236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667353,0.000164796,0.02609428,0.0002292068,0.00002603576,0.0001572225,0.0004541008,0.0002867717,0.005852165],"genre_scores_gemma":[0.9905281,0.00008880203,0.008497021,0.00002959629,0.000008167103,0.00008797134,0.0002200105,0.00001653211,0.0005238339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0721269,"threshold_uncertainty_score":0.1434141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01538250365450178,"score_gpt":0.2430444402696837,"score_spread":0.2276619366151819,"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."}}