{"id":"W4413430772","doi":"10.1029/2025jd044341","title":"Convolutional Neural Network‐Based Insights Into Extreme Precipitation Regional Dynamics Over Central Africa Using Moisture Flux Patterns","year":2025,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Climate variability and models","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alexander von Humboldt-Stiftung","keywords":"Precipitation; Convolutional neural network; Flux (metallurgy); Environmental science; Moisture; Climatology; Dynamics (music); Artificial neural network; Atmospheric sciences; Meteorology; Computer science; Geography; Geology; Artificial intelligence; Physics; Chemistry","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":[0.0002091091,0.000385455,0.0001676379,0.0007728976,0.0001814179,0.0004110803,0.0003060125,0.0002590288,0.001074919],"category_scores_gemma":[0.0007136719,0.0001713734,0.0002542029,0.0007135692,0.0001981234,0.0003847705,0.0002981926,0.000293084,0.00008148272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005965295,"about_ca_system_score_gemma":0.0003686241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04768862,"about_ca_topic_score_gemma":0.04443577,"domain_scores_codex":[0.999964,0.000005678102,0.000001931995,0.00001226029,0.000004346964,0.00001180486],"domain_scores_gemma":[0.9998891,0.00003642625,0.00002864773,0.000008413323,0.00002403441,0.00001329078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002754587,0.0001321544,0.268999,0.00008751699,0.0002695821,0.0006582531,0.0002371652,0.6594373,0.01916587,0.002194853,0.001152306,0.04739054],"study_design_scores_gemma":[0.000006706632,0.00001069923,0.09514278,0.00001424439,0.00001800639,0.00002119142,0.00006505999,0.9031482,0.0006790553,0.0006131334,0.0002721275,0.000008912271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955403,0.000163304,0.002833759,0.0001378934,0.0000101129,0.000006142684,0.0004087185,0.00007551168,0.000824205],"genre_scores_gemma":[0.9992067,0.00004018675,0.0004804615,0.000005375061,0.00000424876,0.000001709446,0.0001423275,0.000003280026,0.0001158381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04768862,"threshold_uncertainty_score":0.09482205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05374864290148033,"score_gpt":0.3153489290552873,"score_spread":0.2616002861538069,"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."}}