{"id":"W4283818380","doi":"10.3390/w14132120","title":"Data-Driven Community Flood Resilience Prediction","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Community resilience; Resilience (materials science); Environmental resource management; Vulnerability (computing); Climate change; Flood mitigation; Categorization; Computer science; Environmental planning; Environmental science; Risk analysis (engineering); Geography; Business; Computer security; Artificial intelligence; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.000441892,0.00005803442,0.00004861466,0.00001592465,0.0006180332,0.00002247556,0.0007031304,0.000009516862,0.006289796],"category_scores_gemma":[0.000002474818,0.00004349856,0.00001304248,0.00006460429,0.00005186248,0.0002750292,0.00357288,0.0001956982,0.0004855613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008551597,"about_ca_system_score_gemma":0.000001857171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001087481,"about_ca_topic_score_gemma":0.0003981962,"domain_scores_codex":[0.9991204,0.0001662453,0.00009252348,0.0001705556,0.0002806338,0.0001696783],"domain_scores_gemma":[0.9992834,0.000007318394,0.00001550809,0.0006624503,0.000001132354,0.00003019603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005625906,0.0008331712,0.2921255,0.00001892969,0.0000543282,0.00003011815,0.004612796,0.1030588,0.03071942,0.000185258,0.5586601,0.009645334],"study_design_scores_gemma":[0.000468761,0.0002327963,0.1788861,0.000002080958,0.00003693319,0.000005583895,0.001123123,0.02693881,0.001447011,0.0006592739,0.7899975,0.0002020656],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.972729,0.000004001626,0.000352812,0.0005437298,0.000262544,0.0001773413,0.00007290163,0.00007123971,0.02578646],"genre_scores_gemma":[0.9950677,0.00000462438,0.0005239552,0.0002405079,0.00001945682,0.00003416227,0.0002620067,0.000006176442,0.003841403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2313374,"threshold_uncertainty_score":0.9946186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02576564714327579,"score_gpt":0.2471501535141425,"score_spread":0.2213845063708667,"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."}}