{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135883,0.0007148478,0.0004885864,0.0004758495,0.0002232771,0.0003582093,0.001164374,0.0005443549,0.001476938],"category_scores_gemma":[0.0008033262,0.0003403354,0.0005741869,0.0005800723,0.0002267569,0.000725258,0.0005261014,0.000880324,0.0004167475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006522979,"about_ca_system_score_gemma":0.0007565507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115673,"about_ca_topic_score_gemma":0.01181457,"domain_scores_codex":[0.9998771,0.00001320335,0.000007312058,0.00004508612,0.00003528582,0.00002203265],"domain_scores_gemma":[0.9997928,0.00005547891,0.00003366105,0.00002861494,0.00006972013,0.00001984211],"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":[0.0001000429,0.00005187062,0.001291947,0.0000713268,0.00008508789,0.000101434,0.00004875397,0.682062,0.01279612,0.005315334,0.003016316,0.2950597],"study_design_scores_gemma":[0.000001308974,0.000005053881,0.00007301946,0.000001130786,0.000003466063,0.000006689146,0.000001104283,0.998732,0.0005403284,0.0004265509,0.0002076076,0.000001698179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02561715,0.0004156692,0.9695379,0.000244528,0.0001346321,0.00004279757,0.0001571639,0.001243319,0.002606774],"genre_scores_gemma":[0.6747478,0.000582233,0.3132388,0.0003912814,0.0002163108,0.0001312032,0.0007911643,0.0001493001,0.009752004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01115673,"threshold_uncertainty_score":0.0221836,"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."}}