{"id":"W3203484495","doi":"10.1002/hyp.14398","title":"Identification of flood seasonality and drivers across Canada","year":2021,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Environment Canada; Environment and Climate Change Canada; Ministry of Earth Sciences","keywords":"Snowmelt; Flood myth; Seasonality; Environmental science; 100-year flood; Climatology; Streamflow; Precipitation; Flooding (psychology); Predictability; Snow; Natural hazard; Flood forecasting; Hydrology (agriculture); Floodplain; Magnitude (astronomy); Hydrometeorology; Physical geography; Geography; Drainage basin; Meteorology; Geology; Ecology; Statistics; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000335782,0.0001891826,0.0002019568,0.001231125,0.001194035,0.000878774,0.0004239662,0.0001332998,0.001569497],"category_scores_gemma":[0.001066608,0.0001453921,0.0003329488,0.002499742,0.0003524601,0.0001806265,0.0007004868,0.0002623271,0.00008533715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0134184,"about_ca_system_score_gemma":0.02085387,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.992136,"about_ca_topic_score_gemma":0.9947632,"domain_scores_codex":[0.9997283,0.00001780875,0.00001266839,0.0000571005,0.00008676838,0.00009737934],"domain_scores_gemma":[0.9991496,0.00009308541,0.00009962634,0.00002694334,0.0004700734,0.0001606544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005445807,0.00001890454,0.9827269,0.00002463158,0.00006665728,0.00008395485,0.0006843095,0.001903876,0.0008883324,0.0005085141,0.001597925,0.01144151],"study_design_scores_gemma":[0.000001456689,0.000004294532,0.9951916,0.000009856831,0.00001368836,0.00001242294,0.0008680209,0.002632046,0.0001083995,0.00007044396,0.001080295,0.000007507361],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936751,0.000121426,0.0003747285,0.000163353,0.000003287975,0.00001807785,0.003676441,0.00002948709,0.00193804],"genre_scores_gemma":[0.9974668,0.0001001989,0.0002181582,0.00001481609,0.000001018535,0.000005985353,0.001347753,0.000004638467,0.0008406151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0134184,"threshold_uncertainty_score":0.09735781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008648492025806018,"score_gpt":0.2330390008290781,"score_spread":0.2243905088032721,"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."}}