{"id":"W2195328857","doi":"10.1038/srep17767","title":"Contribution of human and climate change impacts to changes in streamflow of Canada","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; University of Alberta","keywords":"Streamflow; Climate change; Evapotranspiration; Environmental science; Precipitation; Watershed; Glacier; Climatology; Hydrology (agriculture); Drainage basin; Physical geography; Geography; Ecology; Meteorology; 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":[0.001055124,0.0000564708,0.0001307192,0.00005817145,0.00007040221,0.000006917182,0.0000462914,0.00001934702,0.00003215274],"category_scores_gemma":[0.0000737934,0.00004808974,0.000007525847,0.0001988661,0.0001687703,0.00007331171,0.0001862778,0.00002192945,0.000001275203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007256028,"about_ca_system_score_gemma":0.00001205042,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0805173,"about_ca_topic_score_gemma":0.7172207,"domain_scores_codex":[0.9991398,0.00002138346,0.0001812375,0.0002242415,0.0002235649,0.00020981],"domain_scores_gemma":[0.9995974,0.000006032179,0.0001200131,0.0001755917,0.00001764637,0.00008332194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001100865,0.00003637645,0.9802261,0.00001814126,0.000005698655,0.00008349805,0.001797815,0.0001136548,0.01093497,0.0000388029,0.006232226,0.0005017205],"study_design_scores_gemma":[0.0003605221,0.0002049529,0.9210473,0.00006833189,0.0000187801,0.00001344211,0.0005501973,0.00006737957,0.06978732,0.002530797,0.005166297,0.0001846849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974574,0.00003284124,0.000001160636,0.0005731641,0.0004570765,0.0002163584,0.000003277731,0.000004025913,0.001254716],"genre_scores_gemma":[0.9998045,0.000003803896,0.00001726904,0.00004336259,0.000006110187,0.00001134017,0.000006659203,0.000001989338,0.0001049705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6367034,"threshold_uncertainty_score":0.9256056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191075088020724,"score_gpt":0.2418719918325266,"score_spread":0.2227644830304542,"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."}}