{"id":"W4220926690","doi":"10.5194/egusphere-egu22-12165","title":"Filling in the Gaps: Consistently detecting previously unidentified extreme weather event impacts","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Extreme weather; Damages; Climate change; Event (particle physics); Weather patterns; Climatology; Geography; Environmental resource management; Environmental science; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003288163,0.0003746945,0.0005986308,0.00264836,0.0005443181,0.001570747,0.0007778607,0.0009449503,0.001113554],"category_scores_gemma":[0.0140617,0.0002078026,0.0002837259,0.002257518,0.0003821196,0.001777127,0.001707356,0.0007843504,0.0007668662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003091361,"about_ca_system_score_gemma":0.0006367338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003933899,"about_ca_topic_score_gemma":0.005299449,"domain_scores_codex":[0.9984601,0.0004574407,0.0001974452,0.0004273943,0.000288436,0.0001692684],"domain_scores_gemma":[0.9896718,0.004834345,0.001859206,0.001550582,0.00161472,0.0004692656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000348898,0.000232943,0.8818977,0.00009738795,0.00009851686,0.0003360987,0.0008084232,0.0052218,0.002701967,0.0007276249,0.004122341,0.1034062],"study_design_scores_gemma":[0.00002952711,0.0002705696,0.8499331,0.00011265,0.0001098139,0.0005617695,0.003275934,0.1265059,0.005424858,0.006260603,0.007450013,0.00006512984],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616157,0.0004087922,0.02966974,0.0005877716,0.00009494956,0.00008322296,0.003225949,0.0006823582,0.003631469],"genre_scores_gemma":[0.9789369,0.0001096522,0.01729615,0.00009496091,0.00005610602,0.0000509465,0.003004765,0.00004569481,0.0004048647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003933899,"threshold_uncertainty_score":0.01738971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03509050163183233,"score_gpt":0.2780870136102911,"score_spread":0.2429965119784588,"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."}}