{"id":"W3200090046","doi":"10.1109/tgrs.2021.3108812","title":"Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Meteorological Administration","keywords":"Term (time); Computer science; Radar; Adversarial system; Generative grammar; Remote sensing; Meteorology; Artificial intelligence; Geology; Telecommunications; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009097639,0.0007385081,0.0004732898,0.0003738858,0.0001920776,0.0004192265,0.0007518378,0.0005801541,0.000723538],"category_scores_gemma":[0.001873456,0.0003064478,0.0004952918,0.0003398232,0.0004425457,0.0005156224,0.0006156971,0.001030941,0.0001396222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006035716,"about_ca_system_score_gemma":0.0004234189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006261,"about_ca_topic_score_gemma":0.006318962,"domain_scores_codex":[0.999719,0.00009169037,0.0000135291,0.0000848151,0.0000485219,0.00004246933],"domain_scores_gemma":[0.9988474,0.0007986602,0.000139498,0.00005151912,0.0001252763,0.00003757398],"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":[0.00002350718,0.000009714825,0.0005182236,0.000005157448,0.000009490923,0.0000169338,0.000007680986,0.9936567,0.0002698129,0.0005225927,0.0001132305,0.004846877],"study_design_scores_gemma":[4.320358e-7,0.000002656995,0.00006208094,3.775622e-7,8.747621e-7,0.000001094273,6.022699e-7,0.9997304,0.00006304143,0.0001285517,0.000008984744,8.87688e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2913419,0.0003709815,0.7034165,0.0004601967,0.0001020256,0.00005842258,0.0002507215,0.0006861051,0.003313182],"genre_scores_gemma":[0.9839821,0.00008492548,0.01453988,0.00005748072,0.00002627554,0.00002979962,0.0001805458,0.00001410596,0.001084963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01006261,"threshold_uncertainty_score":0.02000809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04522018560262275,"score_gpt":0.2369544237054673,"score_spread":0.1917342381028446,"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."}}