{"id":"W4385221648","doi":"10.1080/07011784.2023.2238696","title":"Fully integrating probabilistic flood forecasts into the decision-making process across southern Quebec, Canada: some factors to consider","year":2023,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Forêts, de la Faune et des Parcs; Université de Sherbrooke; Université du Québec à Rimouski","funders":"","keywords":"Flood myth; Flood forecasting; Damages; Context (archaeology); Probabilistic logic; Streamflow; Computer science; Consensus forecast; Process (computing); Environmental resource management; Environmental science; Operations research; Geography; Econometrics; Engineering; Artificial intelligence; Cartography; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01090654,0.000483196,0.0004282368,0.0007439985,0.009127569,0.007512945,0.001597929,0.001404863,0.007133475],"category_scores_gemma":[0.02425841,0.0003047626,0.0004640595,0.002421426,0.002211656,0.003275487,0.001872068,0.002086816,0.0005577884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09699818,"about_ca_system_score_gemma":0.2415292,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9969872,"about_ca_topic_score_gemma":0.9983289,"domain_scores_codex":[0.9943158,0.002360925,0.0001742145,0.0003633381,0.001407324,0.001378301],"domain_scores_gemma":[0.9742751,0.00785181,0.0005602551,0.0004265601,0.01349204,0.003394206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004301886,0.0002174855,0.1465263,0.001758199,0.0002029089,0.001851794,0.09943299,0.01558879,0.004767947,0.01557859,0.3455401,0.3681047],"study_design_scores_gemma":[0.00009734874,0.000151749,0.1903014,0.003218327,0.000204457,0.0002109719,0.2489906,0.0220941,0.002164453,0.007735433,0.5242904,0.0005406383],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4027673,0.01074865,0.01586423,0.4655228,0.001008933,0.0006615446,0.004108582,0.0007177035,0.09860034],"genre_scores_gemma":[0.9537889,0.004511956,0.01241071,0.01175034,0.00009325058,0.0001821789,0.0009511306,0.0001276685,0.01618384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09699818,"threshold_uncertainty_score":0.7037744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288005130327092,"score_gpt":0.2434017735232716,"score_spread":0.2305217222200007,"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."}}