{"id":"W1903629707","doi":"10.1002/hyp.9417","title":"Baseflow separation in a small watershed in New Brunswick, Canada, using a recursive digital filter calibrated with the conductivity mass balance method","year":2012,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of New Brunswick","funders":"","keywords":"Baseflow; Hydrograph; Environmental science; Streamflow; Hydrology (agriculture); Statistics; Drainage basin; Mathematics; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002435663,0.0004060138,0.0004042808,0.001069204,0.001937644,0.0008637144,0.0009748218,0.0003473177,0.0006847921],"category_scores_gemma":[0.0005793654,0.0002338099,0.0002691844,0.002221681,0.0006906211,0.0003320739,0.0004073731,0.0003183936,0.0001289815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02036347,"about_ca_system_score_gemma":0.01258629,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9845923,"about_ca_topic_score_gemma":0.988452,"domain_scores_codex":[0.9998052,0.000008782494,0.000007377103,0.00005440959,0.00007706496,0.00004722681],"domain_scores_gemma":[0.9997205,0.00002962911,0.00002105369,0.000015271,0.0001617907,0.00005165443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004561825,0.0005790009,0.8300877,0.0001417991,0.0001484942,0.002225583,0.002308387,0.04164666,0.02988985,0.000969933,0.002363276,0.08918309],"study_design_scores_gemma":[0.00005803461,0.00004191213,0.9091757,0.00001965469,0.00004033815,0.00009057632,0.001039173,0.08369279,0.004001199,0.0001613384,0.001626788,0.00005247567],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971548,0.00004523606,0.0006240487,0.00004176755,0.000002959863,0.00005604899,0.0006635477,0.00006669822,0.001344767],"genre_scores_gemma":[0.9960156,0.00007667006,0.002361608,0.00001859286,0.000001829566,0.00003395203,0.0007444654,0.000009603058,0.0007376812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02036347,"threshold_uncertainty_score":0.147748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0288059725678059,"score_gpt":0.251215865999287,"score_spread":0.2224098934314811,"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."}}