{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002996984,0.000128511,0.0001374938,0.0001206636,0.001044624,0.000178077,0.00003886875,0.00007289103,0.00006087337],"category_scores_gemma":[0.00001272466,0.0001217865,0.00005720208,0.0004068251,0.0002027276,0.0005253426,9.734536e-7,0.0001598701,0.000001417359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001767212,"about_ca_system_score_gemma":0.0001195176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004739041,"about_ca_topic_score_gemma":0.001579931,"domain_scores_codex":[0.9987777,0.0001040459,0.00020251,0.0003482872,0.000360633,0.0002067907],"domain_scores_gemma":[0.9994857,0.0001085705,0.00004625213,0.00009575156,0.0001478834,0.0001158768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000100042,0.00006117819,0.004108902,0.00003334847,0.000239307,0.00008185403,0.001535286,0.248235,0.03547363,0.00002955315,0.00005950437,0.7100424],"study_design_scores_gemma":[0.0002468567,0.00003974831,0.05931155,0.00004602091,0.00009679286,0.00007163922,0.000322392,0.9387063,0.0007038958,0.0002426236,0.00005929666,0.0001528953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3992913,0.00009299457,0.599828,0.0001180063,0.0004666841,0.0000555157,0.00006500247,0.00002020989,0.00006225159],"genre_scores_gemma":[0.9695483,0.000268866,0.0295712,0.0002529697,0.0001595315,3.893688e-8,0.00008200438,0.000003290147,0.0001137324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7098895,"threshold_uncertainty_score":0.8034509,"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."}}