{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001090249,0.0001112884,0.0001255481,0.000008712225,0.0001255494,0.00001609242,0.0001579131,0.00004131734,0.0003991346],"category_scores_gemma":[0.00002974413,0.00008948279,0.0000522672,0.0002142453,0.0001808309,0.0001776154,0.0002885569,0.0001381332,0.000009443981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006898221,"about_ca_system_score_gemma":0.000003326477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051764,"about_ca_topic_score_gemma":0.0006834398,"domain_scores_codex":[0.9991291,0.0000421004,0.0002356747,0.0002580669,0.00016872,0.0001663377],"domain_scores_gemma":[0.9995601,0.00004898766,0.0001400825,0.0001765545,0.000006487564,0.00006777392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001075549,0.00004209788,0.9937724,0.00004668536,0.00001094118,1.170863e-7,0.001378368,0.0005901234,0.002940483,0.00008535182,0.0006087407,0.0005139016],"study_design_scores_gemma":[0.00021491,0.00006089096,0.8738957,0.00002382017,0.00006030481,4.429891e-7,0.0001545946,0.1231168,0.0003184535,0.0003122175,0.001668196,0.0001737158],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995998,0.00001059741,0.001237127,0.001341179,0.00008468881,0.0002337213,0.0002142341,0.00001585194,0.0008645545],"genre_scores_gemma":[0.9991955,0.0001019078,0.0002263268,0.0002950699,0.0000107194,0.00001080935,0.0001062264,0.00000869609,0.00004479687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1225266,"threshold_uncertainty_score":0.4370243,"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."}}