{"id":"W2926224217","doi":"10.1002/hyp.13454","title":"Differences in preferential flow with antecedent moisture conditions and soil texture: Implications for subsurface P transport","year":2019,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Loam; Macropore; Soil science; Soil texture; Antecedent moisture; Soil water; Water content; Silt; Moisture; Infiltration (HVAC); Environmental science; Hydrology (agriculture); Geology; Chemistry; Geotechnical engineering; Materials science; Drainage basin; Geography; Composite material; Runoff curve number; Geomorphology","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":[0.0004524387,0.0002235246,0.0002440817,0.0003370953,0.0001976975,0.0005491354,0.0001739019,0.000220632,0.0008040158],"category_scores_gemma":[0.0007782629,0.0001968974,0.000210242,0.0003463551,0.0003735163,0.0003618923,0.0003094197,0.0002492748,0.00007907143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003660348,"about_ca_system_score_gemma":0.0001777982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004845947,"about_ca_topic_score_gemma":0.004655435,"domain_scores_codex":[0.999769,0.0000585189,0.00001631589,0.00006240827,0.00004599632,0.00004765727],"domain_scores_gemma":[0.9992639,0.0002699891,0.0002450323,0.0000332312,0.00009612016,0.00009160966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006713817,0.0001323832,0.7110536,0.00007732137,0.0001298152,0.000154586,0.0001903934,0.001099602,0.2778956,0.00008058566,0.00009743373,0.008417412],"study_design_scores_gemma":[0.000004312598,0.00007713062,0.9961438,0.000001887088,0.000009337261,0.00003096436,0.00007640846,0.001059653,0.002486372,0.00003450577,0.00007184518,0.000003801396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991671,0.00006219358,0.0004425349,0.00001319639,0.000001520923,0.000007668315,0.00008622916,0.000007273438,0.0002123736],"genre_scores_gemma":[0.9997444,0.00002002064,0.000120128,0.00000684463,0.00000176403,0.000003246132,0.00003237588,0.000002311016,0.00006882926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004845947,"threshold_uncertainty_score":0.009635508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01203373053705095,"score_gpt":0.2148352865128327,"score_spread":0.2028015559757817,"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."}}