{"id":"W4411930246","doi":"10.30574/wjarr.2025.26.3.2504","title":"An individual motion driven CNN-Based AI method for precipitation forecasting Using RADAR Image Sequence","year":2025,"lang":"en","type":"article","venue":"World Journal of Advanced Research and Reviews","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Radar; Sequence (biology); Precipitation; Artificial intelligence; Motion (physics); Computer science; Image (mathematics); Radar imaging; Computer vision; Meteorology; Pattern recognition (psychology); Geography; Telecommunications","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":[0.004376206,0.00009265035,0.000292598,0.000399531,0.0004152248,0.0001009514,0.0001887069,0.0000326339,0.00008562002],"category_scores_gemma":[0.0009096296,0.00006096353,0.00007628504,0.0005957882,0.0001061655,0.0005119422,0.000008635784,0.0003260126,0.000001235612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001486757,"about_ca_system_score_gemma":0.000104748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001571063,"about_ca_topic_score_gemma":0.0001122302,"domain_scores_codex":[0.9980502,0.0007255556,0.000503204,0.0001751052,0.0002771236,0.0002688368],"domain_scores_gemma":[0.99804,0.001103358,0.0002311886,0.0001045529,0.0003681697,0.0001527837],"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":[0.000222491,0.00004045524,0.007075202,0.0001788545,0.000018582,0.000004876828,0.0001133629,0.07622288,0.005871496,0.0002306396,0.0001059169,0.9099153],"study_design_scores_gemma":[0.002160809,0.002167784,0.02375324,0.0009997579,0.00009858646,0.00002039435,0.000358346,0.8672441,0.0008456748,0.07190009,0.03015678,0.0002944456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1231036,0.01299255,0.8603491,0.001247813,0.0002868849,0.001387413,0.00005764141,0.00001081671,0.0005641594],"genre_scores_gemma":[0.3050638,0.0006232043,0.6938305,0.00023023,0.0001217945,0.000003479406,0.0000383322,0.000002971476,0.00008561686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9096208,"threshold_uncertainty_score":0.3193616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2179797617614266,"score_gpt":0.4517588933514331,"score_spread":0.2337791315900065,"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."}}