{"id":"W1969642066","doi":"10.1002/hyp.7625","title":"Detection of trends in hydrological extremes for Canadian watersheds","year":2010,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Environmental science; Streamflow; Trend analysis; Magnitude (astronomy); Climate change; Precipitation; Hydrology (agriculture); Flow (mathematics); Resampling; Climatology; Meteorology; Drainage basin; Geography; Statistics; Geology; Mathematics; Cartography; Oceanography","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":[0.0006171362,0.0001673686,0.0002444166,0.002581042,0.001028253,0.0006415148,0.0003730762,0.0001790242,0.0007481438],"category_scores_gemma":[0.002566099,0.0001265151,0.0002357033,0.003891753,0.0003814746,0.0002721697,0.0004862818,0.0002209459,0.00005999694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006763396,"about_ca_system_score_gemma":0.006893972,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.953981,"about_ca_topic_score_gemma":0.9711202,"domain_scores_codex":[0.9995885,0.00002320479,0.00002224703,0.00006446605,0.0001917619,0.0001097215],"domain_scores_gemma":[0.9985671,0.0002094037,0.0002604948,0.00005627598,0.000756432,0.0001502338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001163528,0.00001964154,0.9693094,0.00003116917,0.00006534954,0.00008760445,0.0006834288,0.002217256,0.001833287,0.0002414462,0.000808464,0.02458649],"study_design_scores_gemma":[0.000001321124,0.000008446395,0.9979989,0.000002657465,0.000006917468,0.00001515651,0.000167779,0.001056476,0.0001883954,0.00002182743,0.0005266401,0.000005399159],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967461,0.00008504513,0.0002574735,0.00003207806,0.000001324926,0.00000986592,0.002049038,0.00002043493,0.0007985584],"genre_scores_gemma":[0.9967818,0.00007575843,0.0004859582,0.000005221492,0.000001488524,0.000008013456,0.002385478,0.000003007216,0.0002531823],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04601896,"threshold_uncertainty_score":0.0925799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186879145047745,"score_gpt":0.2348271111914382,"score_spread":0.2161391966866637,"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."}}