{"id":"W4283735286","doi":"10.1016/j.jhydrol.2022.128120","title":"Comparing alternative conceptual models for tile drains and soil heterogeneity for the simulation of tile drainage in agricultural catchments","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Innovationsfonden; Nationale Geologiske Undersøgelser for Danmark og Grønland; Université du Québec à Chicoutimi; Aarhus Universitet","keywords":"Tile drainage; Tile; Hydrology (agriculture); Drainage; Surface runoff; Environmental science; Ponding; Drainage basin; Geology; Geotechnical engineering; Soil science; Soil water; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000515693,0.00006995407,0.0002044433,0.00003607218,0.0002043651,0.00000348234,0.0001472117,0.00002155205,0.00002079363],"category_scores_gemma":[0.00001871665,0.00004706314,0.00005905308,0.00004519662,0.0002068606,0.0001226612,0.0002229378,0.0001015425,3.216311e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006566116,"about_ca_system_score_gemma":0.000003110558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005752558,"about_ca_topic_score_gemma":0.0001234226,"domain_scores_codex":[0.9992855,0.0000857574,0.0002627222,0.000106313,0.0001074162,0.000152305],"domain_scores_gemma":[0.9993894,0.0002713168,0.0002589005,0.00005063437,0.00001118796,0.00001858789],"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":[0.0001892216,0.00005854932,0.01397777,0.00000435219,0.00006859727,0.000001845939,0.003395385,0.9812352,0.0005700015,0.0002517026,0.0001586529,0.00008873364],"study_design_scores_gemma":[0.002379764,0.0008750323,0.02741757,0.000003470261,0.00007465277,0.00001441816,0.001296456,0.9555281,0.0005097786,0.01080224,0.00100505,0.00009340411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934472,0.00007660001,0.005111638,0.0006212138,0.0001288843,0.0002986868,0.000009962501,0.000001906177,0.0003038412],"genre_scores_gemma":[0.9995927,0.000007399324,0.00009047452,0.0001967753,0.0000259787,0.00003692507,0.000002841171,0.000003423518,0.00004350667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02570704,"threshold_uncertainty_score":0.1919179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03800506566935889,"score_gpt":0.2742573361708517,"score_spread":0.2362522705014928,"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."}}