{"id":"W3093984799","doi":"10.3390/w12123353","title":"CliGAN: A Structurally Sensitive Convolutional Neural Network Model for Statistical Downscaling of Precipitation from Multi-Model Ensembles","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Climate variability and models","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Downscaling; Computer science; Precipitation; Climate model; Environmental science; Artificial neural network; Convolutional neural network; Similarity (geometry); Meteorology; Climatology; Artificial intelligence; Climate change; Image (mathematics); Geography; Geology","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":[0.0005719682,0.0006779081,0.0004370275,0.0002813388,0.000221003,0.0003740124,0.001290735,0.0006114737,0.001678979],"category_scores_gemma":[0.001625351,0.0003656184,0.0005630578,0.0002952827,0.000409972,0.0006366801,0.0009452673,0.001344475,0.0003356108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007088735,"about_ca_system_score_gemma":0.0007853456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01007047,"about_ca_topic_score_gemma":0.01564856,"domain_scores_codex":[0.9998429,0.00003581399,0.000007343155,0.00004746312,0.00004531235,0.0000210837],"domain_scores_gemma":[0.999705,0.0001296963,0.00004152125,0.00004519959,0.00005930686,0.00001928554],"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":[0.0000253083,0.00001076008,0.0004454728,0.00001447698,0.00003143877,0.00002840189,0.00001338676,0.9752166,0.001489671,0.003653876,0.001112592,0.01795806],"study_design_scores_gemma":[0.000001177518,0.000003831712,0.00004609701,0.000001133652,0.00000209722,0.00000394537,6.100983e-7,0.9985618,0.0003094723,0.0008074519,0.0002609995,0.00000148205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0218407,0.000221596,0.9731557,0.0002600132,0.00007123615,0.00003964148,0.0003314244,0.001481394,0.002598251],"genre_scores_gemma":[0.7812635,0.0003631517,0.2069823,0.0003453018,0.0000935494,0.0001919472,0.001397368,0.0004502118,0.00891254],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01007047,"threshold_uncertainty_score":0.0200237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0455564253897066,"score_gpt":0.2614170211083944,"score_spread":0.2158605957186878,"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."}}