{"id":"W1995258565","doi":"10.1016/j.apgeochem.2007.03.046","title":"The contribution of rain-on-snow events to annual NO3-N export from a forested catchment in south-central Ontario, Canada","year":2007,"lang":"en","type":"article","venue":"Applied Geochemistry","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Environment","keywords":"Snow; Environmental science; Precipitation; Surface runoff; Climate change; Drainage basin; Hydrology (agriculture); Alkalinity; STREAMS; Ecosystem; Snowmelt; Acid rain; Surface water; Physical geography; Ecology; Geography; Geology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002113179,0.0001426281,0.0001421918,0.00000886171,0.00007698155,0.000006295125,0.0002447568,0.00007289794,0.00006090053],"category_scores_gemma":[0.00002209874,0.0001171864,0.00003027043,0.0001254844,0.00005225289,0.00001803479,0.0001167135,0.0001469237,0.00001137399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134052,"about_ca_system_score_gemma":0.000107065,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6085789,"about_ca_topic_score_gemma":0.8209194,"domain_scores_codex":[0.998529,0.000005845699,0.0003032876,0.0002762023,0.0004173822,0.0004683407],"domain_scores_gemma":[0.999388,0.00008225753,0.00009607993,0.0002675107,0.00001005318,0.0001560411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002840723,0.00008611936,0.9922213,0.000001774946,0.00001331112,0.00001276706,0.001655884,0.0004474167,0.00281744,0.00002314196,0.001238252,0.001198553],"study_design_scores_gemma":[0.001163481,0.00002470686,0.8765833,0.00002199536,0.00001020034,0.000001043333,0.001055649,0.00005102488,0.1151847,0.001161527,0.004514299,0.0002280489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973709,0.000004299142,0.0001105447,0.0001036011,0.0001022471,0.0002457748,0.0000755916,0.000008586635,0.001978483],"genre_scores_gemma":[0.9993863,6.827098e-7,0.00004775534,0.0001742971,0.0000317888,0.0000276161,0.0001723678,0.000006666959,0.0001525718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2123405,"threshold_uncertainty_score":0.4778723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002988370935846,"score_gpt":0.1795341801137814,"score_spread":0.1765458091779354,"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."}}