{"id":"W1998686521","doi":"10.1002/hyp.6361","title":"Capturing temporal variability for estimates of annual hydrochemical export from a first‐order agricultural catchment in southern Ontario, Canada","year":2006,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Wilfrid Laurier University","keywords":"Nutrient; Environmental science; Watershed; Phosphorus; Nitrate; Drainage basin; Hydrology (agriculture); Sampling (signal processing); Structural basin; Agriculture; Eutrophication; Magnitude (astronomy); Storm; Geography; Ecology; Biology; Geology; Chemistry; Meteorology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002242046,0.0002076814,0.0001948246,0.0007741801,0.0007657037,0.000720101,0.0004266565,0.0001692964,0.0009699718],"category_scores_gemma":[0.001210997,0.000143603,0.0001840841,0.001557797,0.0002416405,0.0002391025,0.0003438775,0.000145922,0.0001196403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008561207,"about_ca_system_score_gemma":0.005975256,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9811639,"about_ca_topic_score_gemma":0.9914062,"domain_scores_codex":[0.9998382,0.000008074199,0.000008379216,0.00004340107,0.00006202679,0.00003993324],"domain_scores_gemma":[0.9993035,0.00009066901,0.0001223302,0.00003269118,0.0003714169,0.00007943303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007185677,0.00002356125,0.9790563,0.00003880122,0.00005150056,0.000132282,0.001181492,0.002890027,0.002556484,0.00007907434,0.0009262204,0.0129924],"study_design_scores_gemma":[0.000002836714,0.000005231761,0.9957085,0.000004880558,0.000007490401,0.00001242422,0.0003369575,0.002820264,0.0001374075,0.0000157237,0.0009437067,0.000004430579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967919,0.00006766018,0.0003749781,0.00004092839,0.000001792224,0.00001818023,0.001590171,0.00002259513,0.001091827],"genre_scores_gemma":[0.996709,0.00007748848,0.0004344321,0.00001072871,0.000001922955,0.00001546591,0.001695954,0.000006927766,0.001048037],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01883608,"threshold_uncertainty_score":0.06211621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005954388146474299,"score_gpt":0.1798208052624278,"score_spread":0.1738664171159535,"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."}}