{"id":"W4389405983","doi":"10.1007/s10584-023-03644-8","title":"Understanding changes in the timing of heavy storms: a regional case study of climate change impacts","year":2023,"lang":"en","type":"article","venue":"Climatic Change","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Storm; Environmental science; Climate change; Flood myth; Precipitation; Climatology; Hydrology (agriculture); Physical geography; Meteorology; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001362245,0.0002725932,0.0002605382,0.0008468404,0.0005965693,0.001219502,0.0006225532,0.0008903153,0.001227196],"category_scores_gemma":[0.002746823,0.0001718972,0.0005771957,0.001933787,0.0005512016,0.001158637,0.0005342952,0.0007117185,0.000115321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001758883,"about_ca_system_score_gemma":0.0009911776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1161737,"about_ca_topic_score_gemma":0.1854764,"domain_scores_codex":[0.9995592,0.0002092646,0.00001882344,0.00007512907,0.00005003525,0.00008752915],"domain_scores_gemma":[0.9986966,0.0007153761,0.0002413958,0.0001116862,0.0001310948,0.0001037662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007391577,0.001075899,0.6666561,0.000325277,0.0007095443,0.007533479,0.01109917,0.1977639,0.01610659,0.01126252,0.005440112,0.08128826],"study_design_scores_gemma":[0.00007534559,0.0004111979,0.795246,0.00006618119,0.0003761726,0.0009193677,0.02960832,0.148227,0.00394694,0.005988896,0.01504994,0.00008452465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935725,0.0002222825,0.001852597,0.0008122404,0.000005004462,0.00001950673,0.0005865155,0.00002542236,0.002903964],"genre_scores_gemma":[0.9958759,0.0003109651,0.003046978,0.00004753221,0.00001023806,0.000008116802,0.0002703407,0.00001518014,0.0004147579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1161737,"threshold_uncertainty_score":0.2309949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3157011131074934,"score_gpt":0.3452153044483312,"score_spread":0.02951419134083783,"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."}}