{"id":"W2599909510","doi":"10.1175/bams-d-15-00256.1","title":"On the Climatological Use of Radar Data Mosaics: Possibilities and Challenges","year":2017,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Precipitation; Radar; Terrain; Meteorology; Climatology; Environmental science; Spatial distribution; Convection; Geography; Geology; Computer science; Remote sensing; Cartography","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001155242,0.0001665692,0.0004588593,0.000007508298,0.0006226626,0.00005855703,0.001602031,0.00008088625,0.001247592],"category_scores_gemma":[0.003007957,0.00006912013,0.0001908748,0.00005230153,0.004105639,0.00004822557,0.0004366615,0.0002635416,0.00001329062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002210396,"about_ca_system_score_gemma":0.00001249893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009580256,"about_ca_topic_score_gemma":0.00003842001,"domain_scores_codex":[0.9981403,0.0005331031,0.0003302643,0.0004021212,0.0003228084,0.0002714278],"domain_scores_gemma":[0.993365,0.004409163,0.0005926222,0.001512242,0.00004249572,0.00007843148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001452748,0.0006055598,0.7490329,0.0001601948,0.0006590954,0.00000860646,0.0008721407,0.001562518,0.0003066334,0.08481386,0.02803468,0.132491],"study_design_scores_gemma":[0.0001800749,0.0006424997,0.9600072,0.00001061864,0.00004328538,0.000002888361,0.0003918613,0.001860704,0.00001930095,0.01984865,0.01686031,0.0001326363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720327,0.0009707191,0.00001755881,0.02498196,0.00005853808,0.0002180766,0.0002428934,0.00001692907,0.001460666],"genre_scores_gemma":[0.9915894,0.001830284,0.004333409,0.002131266,0.00003000561,0.000001604072,0.00001003685,0.000002674635,0.00007129702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2109743,"threshold_uncertainty_score":0.9996654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1458821726934938,"score_gpt":0.2721604311373374,"score_spread":0.1262782584438436,"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."}}