{"id":"W7096801729","doi":"","title":"1 P4.4 Three-Dimensional Radar Mosaic Integrating WSR-88Ds and Canadian Radar Network","year":2015,"lang":"en","type":"article","venue":"","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mosaic; Aviation; Radar; 3D radar; Air traffic control radar beacon system; Radar configurations and types; Man-portable radar","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007737438,0.0006230967,0.0004504369,0.001734748,0.0012437,0.0014389,0.001597931,0.000453635,0.02851474],"category_scores_gemma":[0.001164231,0.0006201247,0.0007067416,0.002330496,0.0003867196,0.0007440633,0.000942036,0.0008900974,0.009323929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003751545,"about_ca_system_score_gemma":0.01025343,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6563228,"about_ca_topic_score_gemma":0.7092273,"domain_scores_codex":[0.9994642,0.00002088041,0.000008701885,0.0000663127,0.0003229246,0.000117016],"domain_scores_gemma":[0.9989603,0.0000269257,0.00002484086,0.0001599999,0.000634875,0.0001931203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009575192,0.0004086648,0.03831984,0.0002169144,0.0002848427,0.0004110078,0.0005227611,0.153779,0.06866337,0.01331255,0.4624681,0.2606555],"study_design_scores_gemma":[0.0003792988,0.0001189416,0.06505834,0.0000553079,0.00008986439,0.000180182,0.000273494,0.5351045,0.03449906,0.005153269,0.3588366,0.0002510541],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1286038,0.0003064334,0.2609099,0.001462841,0.0007992309,0.002393349,0.2834804,0.09522725,0.2268169],"genre_scores_gemma":[0.3530838,0.000330916,0.3465371,0.0003054862,0.0001068347,0.0006341852,0.2452602,0.007383919,0.0463576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3436772,"threshold_uncertainty_score":0.6914023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458950091298972,"score_gpt":0.218685123196469,"score_spread":0.1840956222834793,"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."}}