{"id":"W2022175763","doi":"10.4236/jwarp.2012.42007","title":"Climate Change and Heavy Rainfall-Related Water Damage Insurance Claims and Losses in Ontario, Canada","year":2012,"lang":"en","type":"article","venue":"Journal of Water Resource and Protection","topic":"Climate variability and models","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; Environment and Climate Change Canada","funders":"Government of Canada","keywords":"Downscaling; Environmental science; Climatology; Climate change; Precipitation; Flood myth; Index (typography); Meteorology; Hydrology (agriculture); Geography; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007366478,0.00009844652,0.0001491882,0.00004332914,0.0001222662,0.000034044,0.00003759904,0.00006998063,0.0001677907],"category_scores_gemma":[0.000005057156,0.00005954784,0.00001537343,0.00003432893,0.00006977939,0.0004619462,0.000089983,0.0002671262,0.000001908698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429102,"about_ca_system_score_gemma":0.000005388451,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5112829,"about_ca_topic_score_gemma":0.6110337,"domain_scores_codex":[0.9991406,0.000077966,0.0002541406,0.0001129992,0.0001387606,0.0002755205],"domain_scores_gemma":[0.9997468,0.00001148706,0.00006218204,0.00006266824,0.000006265895,0.0001106166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000552504,0.00009759419,0.9365997,0.00009750242,0.00001806035,0.0000248905,0.02681172,0.0002950666,0.02792166,0.000006406991,0.00005035245,0.007524522],"study_design_scores_gemma":[0.00121031,0.0002720186,0.9584504,0.000129438,0.00002332975,0.0004576646,0.0003626505,0.0003602409,0.01143212,0.0005490445,0.02650143,0.0002513564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986105,0.0000836348,0.000005791612,0.0008032605,0.00006256692,0.0002094881,0.000001897516,0.000003542301,0.0002193501],"genre_scores_gemma":[0.9996222,0.00007429801,0.00003162015,0.0001482847,0.0000350375,0.000007317216,0.000001335436,0.000006604349,0.00007327382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09975075,"threshold_uncertainty_score":0.4919715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264950357054989,"score_gpt":0.1995836590647688,"score_spread":0.1769341554942189,"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."}}