{"id":"W2060160891","doi":"10.1016/j.biosystemseng.2007.06.012","title":"Tine-influenced infiltration patterns and informing timing of liquid amendment applications using brilliant blue dye tracers","year":2007,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture, Food and Rural Affairs; University of Guelph; Agriculture and Agri-Food Canada; University of Ottawa","funders":"University of Guelph","keywords":"Loam; Slurry; Infiltration (HVAC); Infiltrometer; Tillage; Aeration; Environmental science; Liquid manure; Plough; Soil science; Soil water; Hydrology (agriculture); Environmental engineering; Materials science; Geotechnical engineering; Manure; Geology; Waste management; Hydraulic conductivity; Composite material; Agronomy; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005269118,0.0003574702,0.000439196,0.000312795,0.0002118488,0.001418799,0.0002728275,0.0004966519,0.0007547292],"category_scores_gemma":[0.00125509,0.0003235349,0.0001713082,0.000420262,0.0003496488,0.0006049094,0.000330926,0.0005801475,0.0002001793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006806118,"about_ca_system_score_gemma":0.0007753758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007425575,"about_ca_topic_score_gemma":0.01052747,"domain_scores_codex":[0.9996679,0.00007002085,0.00002613919,0.0001069148,0.00006417581,0.0000648809],"domain_scores_gemma":[0.9994239,0.0002438664,0.000132557,0.00003122562,0.0001217227,0.00004667692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003667205,0.00002402248,0.001340632,0.00002931536,0.000004411053,0.00001235204,0.00003455063,0.0004630258,0.9948255,0.00005072956,0.00001679921,0.002831984],"study_design_scores_gemma":[0.00002718061,0.0002021077,0.004717825,0.000006498297,0.00001917411,0.00001456774,0.00006866343,0.01096952,0.9828842,0.000061493,0.001012837,0.00001598446],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990069,0.0002714718,0.008605855,0.00005325396,0.00002459466,0.00003726962,0.0001726511,0.0001509453,0.0006150318],"genre_scores_gemma":[0.9923303,0.0002510972,0.005749098,0.00002655095,0.000003730439,0.00003917003,0.0001166775,0.00004812761,0.001435308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007425575,"threshold_uncertainty_score":0.01476473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919934947187397,"score_gpt":0.2254002000841595,"score_spread":0.2062008506122855,"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."}}