{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003519095,0.000163591,0.0002414574,0.0001604757,0.0001642858,0.000009471699,0.0002640205,0.0002299282,0.001511313],"category_scores_gemma":[0.0001766387,0.0001162176,0.00005584469,0.000405426,0.000478855,0.0001391346,0.0001075476,0.0002166376,0.00004523661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003216115,"about_ca_system_score_gemma":0.000008214054,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005332849,"about_ca_topic_score_gemma":0.1927687,"domain_scores_codex":[0.9987184,0.00003258911,0.0002525509,0.0003994856,0.0001181653,0.0004787669],"domain_scores_gemma":[0.9995828,0.00008452219,0.00006291323,0.0001485954,0.00001196837,0.0001092412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006857503,0.0007455466,0.8513743,0.0001334149,0.00007494085,0.00007150271,0.001139972,0.005412463,0.09291981,0.0006304035,0.002002213,0.04480963],"study_design_scores_gemma":[0.003183951,0.002776843,0.7146221,0.00001530146,0.0001362014,0.00003805423,0.0001406177,0.01152581,0.06380841,0.05957898,0.1428296,0.001344086],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855856,0.00002389588,0.0001972381,0.00182379,0.0001163443,0.0001998731,0.000005456666,0.00004491608,0.01200283],"genre_scores_gemma":[0.9988016,0.00001218247,0.0002501051,0.000450974,0.00003303332,0.0001133825,0.0000124493,0.000007461264,0.0003188412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1874358,"threshold_uncertainty_score":0.9994015,"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."}}