{"id":"W3003092221","doi":"10.1007/s00382-020-05139-z","title":"Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings","year":2020,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"State Key Laboratory of Drug Research; European Centre for Medium-Range Weather Forecasts; Göteborgs Universitet; Graduate Research and Innovation Projects of Jiangsu Province; China Meteorological Administration; Chinese Academy of Sciences; Swedish Foundation for International Cooperation in Research and Higher Education","keywords":"Precipitation; Climatology; Moisture; Westerlies; Environmental science; Atmospheric sciences; Humidity; Latitude; Geology; Meteorology; Geography","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":[0.0002173506,0.0002879183,0.0002043917,0.0003367442,0.0002791641,0.0004478643,0.0001834434,0.0001860025,0.000942691],"category_scores_gemma":[0.0003803322,0.0001813811,0.0004208311,0.0003044571,0.000323837,0.0003114494,0.0004371296,0.0001577217,0.0000533077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006406811,"about_ca_system_score_gemma":0.0005271625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04815113,"about_ca_topic_score_gemma":0.05267252,"domain_scores_codex":[0.9999038,0.00002291667,0.000005919977,0.00002289294,0.00001430706,0.00003007562],"domain_scores_gemma":[0.9998667,0.00002371288,0.00003975639,0.00001219277,0.0000255961,0.00003194614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001560332,0.00007039375,0.9346769,0.00004059336,0.000198632,0.0007824993,0.0002187087,0.03510366,0.02181902,0.0004084083,0.0004176488,0.006107615],"study_design_scores_gemma":[0.00001521126,0.00003882028,0.9634566,0.000003510662,0.00003889532,0.00005376663,0.0001832608,0.03521542,0.0006385825,0.00007382732,0.0002701441,0.00001185123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994333,0.00002485978,0.0001002337,0.00002617037,0.000002841648,0.0000019929,0.00004271546,0.00000864818,0.0003590988],"genre_scores_gemma":[0.9998655,0.00001369274,0.00003018867,0.000002893932,0.000001804124,7.604239e-7,0.0000263059,0.000001183651,0.0000577107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04815113,"threshold_uncertainty_score":0.09574175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01669802327613078,"score_gpt":0.2349399663607967,"score_spread":0.2182419430846659,"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."}}