{"id":"W3091077828","doi":"10.1103/physrevfluids.5.094502","title":"Merging of long rows of plumes: Crosswinds, multiple rows, and applications to cooling towers","year":2020,"lang":"en","type":"article","venue":"Physical Review Fluids","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Crosswind; Row; Plume; Entrainment (biomusicology); Meteorology; Environmental science; Atmospheric sciences; Geology; Computer science; Physics; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007485435,0.0001015409,0.0003319331,0.000006656838,0.0000600793,0.00000513073,0.000141201,0.00000907603,0.00006617209],"category_scores_gemma":[0.0001148533,0.00008387434,0.00007965806,0.0002861705,0.0001290544,0.00007238096,0.0002203055,0.00005699969,0.00006435374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001279668,"about_ca_system_score_gemma":0.000004708847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003339336,"about_ca_topic_score_gemma":0.000002885468,"domain_scores_codex":[0.9992266,0.00001771581,0.0002192608,0.0002216448,0.0001788724,0.000135924],"domain_scores_gemma":[0.9996135,0.0000720512,0.0000520797,0.0001353498,0.00001091641,0.000116082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005012663,0.0005307838,0.05318709,0.004963591,0.0001309373,0.000004988919,0.004832445,0.001765157,0.4738925,0.001533779,0.01428651,0.4448221],"study_design_scores_gemma":[0.002203234,0.001085814,0.149335,0.006751419,0.0008001797,0.00000657457,0.001229533,0.02293508,0.199558,0.0008280427,0.6130646,0.002202554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9446173,0.03366557,0.009609557,0.004516583,0.00004002926,0.001655079,0.00005426653,0.00006385452,0.005777783],"genre_scores_gemma":[0.9961027,0.002683661,0.0005242249,0.0005491734,0.00006858391,0.00004479578,0.000002192079,0.000008082406,0.00001660249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5987781,"threshold_uncertainty_score":0.3420297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831350308171314,"score_gpt":0.2827731583566223,"score_spread":0.2644596552749092,"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."}}