{"id":"W2088727978","doi":"10.1080/02626667.2012.728705","title":"Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow","year":2012,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; McMaster University; Environment and Climate Change Canada; University of Waterloo","funders":"","keywords":"Streamflow; Climate change; Variety (cybernetics); Environmental science; Scale (ratio); Hydrological modelling; Data collection; Environmental resource management; Climatology; Hydrology (agriculture); Computer science; Geography; Drainage basin; Statistics; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004171952,0.0005331166,0.0006654064,0.0002411301,0.00213941,0.0001495296,0.001496623,0.0004871417,0.002178582],"category_scores_gemma":[0.0001768435,0.0003613715,0.0001061209,0.001281271,0.001279745,0.0008310605,0.002245152,0.001273498,0.0002169374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949259,"about_ca_system_score_gemma":0.00001406109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001120944,"about_ca_topic_score_gemma":0.0004895958,"domain_scores_codex":[0.9941785,0.0007227545,0.0006816335,0.0009375763,0.0007234397,0.002756059],"domain_scores_gemma":[0.9983759,0.0002667802,0.0003777633,0.0003364693,0.0000201932,0.0006229183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006076696,0.0001687644,0.5190398,0.000001818679,0.00001162893,0.00004948994,0.0001132386,0.4792065,0.0002821096,0.0002104501,0.000163625,0.0006918448],"study_design_scores_gemma":[0.0007647363,0.002997898,0.3087834,0.00006202904,0.00008947813,0.0002521045,0.0001899743,0.6782773,0.00006104806,0.001591204,0.005806884,0.001124026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840625,0.0003035988,0.00202698,0.001821935,0.00056298,0.0003905379,0.000003185176,0.0001029778,0.01072531],"genre_scores_gemma":[0.9924124,0.0009762273,0.002531621,0.003632467,0.0003283991,0.00004430912,0.00000496297,0.00001502896,0.0000545785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2102564,"threshold_uncertainty_score":0.9998838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1066085396296348,"score_gpt":0.30743036660208,"score_spread":0.2008218269724452,"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."}}