{"id":"W4409434768","doi":"10.1016/j.jhydrol.2025.133295","title":"Estimating rainfall intensity from surveillance audio: A hybrid model-data-driven framework","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Education and Child Care","funders":"China Scholarship Council; Nanjing Institute of Technology; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Intensity (physics); Environmental science; Meteorology; Computer science; Climatology; Remote sensing; 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":[0.0007346523,0.0008113335,0.001151897,0.0009743622,0.0002072729,0.0008801214,0.001438698,0.0008700544,0.0009160317],"category_scores_gemma":[0.002338362,0.0006027489,0.0009655913,0.0009200383,0.0003122465,0.000965583,0.0008688677,0.0008492076,0.0003971664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004812257,"about_ca_system_score_gemma":0.001014769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01091994,"about_ca_topic_score_gemma":0.01324176,"domain_scores_codex":[0.9996481,0.00006112381,0.00002282601,0.0001116793,0.00009397756,0.00006213986],"domain_scores_gemma":[0.999315,0.0003497821,0.00007266351,0.00006649321,0.000154614,0.00004136003],"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":[0.0003072313,0.0003247566,0.00566829,0.0001154021,0.0001654024,0.0001280881,0.00004190491,0.7776231,0.0161317,0.002880404,0.00130221,0.1953115],"study_design_scores_gemma":[0.000005839125,0.0000140595,0.0004466631,0.000001925139,0.000008413329,0.00000963713,0.0000030121,0.9981026,0.0007536263,0.000545456,0.0001038078,0.000005112494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06197651,0.0003193413,0.9349469,0.0002181825,0.00005968055,0.00005947906,0.0005851717,0.001074385,0.000760417],"genre_scores_gemma":[0.819207,0.0003733702,0.1755673,0.0001220137,0.0002075686,0.0001744436,0.002225863,0.0001412407,0.001981173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01091994,"threshold_uncertainty_score":0.02171272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02935224389474499,"score_gpt":0.26582416452321,"score_spread":0.236471920628465,"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."}}