{"id":"W2165068138","doi":"10.4141/cjss2012-079","title":"Generation of soil drainage equations from an artificial neural network-analysis approach","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Soil Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of New Brunswick","funders":"","keywords":"Drainage; Hydrology (agriculture); Topographic Wetness Index; Soil science; Environmental science; Watershed; Soil water; Digital elevation model; Drainage density; Mean squared error; Geology; Geotechnical engineering; Mathematics; Statistics; Remote sensing; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007733388,0.00009034617,0.0001788256,0.0002158065,0.0004289035,0.0002058071,0.0004859913,0.00003597347,0.0007746369],"category_scores_gemma":[0.0001928257,0.00008228731,0.0000751411,0.001361844,0.0006366962,0.0007685453,0.00003274315,0.0001183706,0.00002352315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001795639,"about_ca_system_score_gemma":0.0003630267,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1874831,"about_ca_topic_score_gemma":0.2559393,"domain_scores_codex":[0.9985197,0.00005486959,0.0003943708,0.0002126688,0.0004471711,0.0003712356],"domain_scores_gemma":[0.9986855,0.00004395497,0.0002937576,0.0002142077,0.0001104367,0.000652119],"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":[0.000001132224,0.0000211612,0.02893545,0.000001006887,0.00002995403,0.000006764837,0.001359554,0.943693,0.007980062,0.001481077,0.0007567753,0.0157341],"study_design_scores_gemma":[0.00005939908,0.00005120637,0.1799989,0.000003149383,0.00009021804,0.000003579779,0.000350392,0.8151535,0.000270248,0.003861045,0.00004595632,0.000112433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627962,0.00002991807,0.03358483,0.0002740058,0.0002519453,0.00007004085,0.00001668234,0.000002679166,0.002973719],"genre_scores_gemma":[0.9948687,0.000001245911,0.004694293,0.0001815836,0.0001953886,0.000002409237,0.00001117599,0.000004837146,0.00004035241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1510634,"threshold_uncertainty_score":0.8481731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04355317530860765,"score_gpt":0.2410679042490634,"score_spread":0.1975147289404558,"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."}}