{"id":"W2794145264","doi":"","title":"Capturing Plume Rise and Dispersion with WRF-Fire: an RxCADRE Case Study","year":2017,"lang":"en","type":"article","venue":"97th American Meteorological Society Annual Meeting","topic":"Fire dynamics and safety research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Meteorology; Plume; Environmental science; Weather Research and Forecasting Model; Atmospheric sciences; Dispersion (optics); Atmospheric dispersion modeling; Climatology; Geography; Geology; Air pollution; Physics; Optics; Chemistry","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":[0.0008376669,0.0003138865,0.0004587594,0.0000202041,0.001254813,0.0002482065,0.0003949951,0.0001120047,0.00001494065],"category_scores_gemma":[0.0001314586,0.00023495,0.0001098489,0.0001399399,0.000757935,0.0003664319,0.0003084366,0.0006357182,0.000004024223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007031816,"about_ca_system_score_gemma":0.0000151131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002664756,"about_ca_topic_score_gemma":0.0002875231,"domain_scores_codex":[0.9980149,0.0001414398,0.0002597891,0.0005412967,0.0003657663,0.0006768215],"domain_scores_gemma":[0.9987388,0.0001580343,0.0001221115,0.0005320595,0.00007730279,0.000371655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006091497,0.00102022,0.5392877,0.0002610397,0.0009927048,0.008085329,0.04004369,0.01612559,0.001936686,0.000147344,0.0002743315,0.3912162],"study_design_scores_gemma":[0.002364567,0.005830813,0.1845944,0.00007523072,0.0001799365,0.0009873493,0.1686214,0.6349846,0.00005409701,0.00006437908,0.0008022061,0.001440951],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981753,0.0002264847,0.0002351994,0.0002638177,0.00005329515,0.000356556,0.00003409708,0.0002405831,0.0004147247],"genre_scores_gemma":[0.9956574,0.000169639,0.003879606,0.00005644702,0.0001248222,0.00003619449,0.000004136707,0.00004715674,0.00002456494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6188591,"threshold_uncertainty_score":0.9651133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01329359447762062,"score_gpt":0.2666078021606771,"score_spread":0.2533142076830565,"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."}}