{"id":"W7116708567","doi":"10.1016/j.wse.2025.12.005","title":"Enhancing mean flow characteristics and reducing turbulence in channel transition using honeycomb","year":2025,"lang":"en","type":"article","venue":"Water Science and Engineering","topic":"Hydraulic flow and structures","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Turbulence; Honeycomb; Flow (mathematics); Channel (broadcasting); Honeycomb structure; Open-channel flow; Secondary flow; Mean flow","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":[0.0002829355,0.0001246978,0.0001418577,0.000292172,0.00009426602,0.00009850982,0.00008493338,0.00004493453,0.000001372245],"category_scores_gemma":[0.00001624149,0.0001087809,0.00001023251,0.0002819621,0.00005454836,0.0003215009,0.00003315821,0.0001275855,3.922052e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005930628,"about_ca_system_score_gemma":0.00001449235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003122574,"about_ca_topic_score_gemma":0.000006746107,"domain_scores_codex":[0.9991966,0.000003560441,0.000160409,0.0002003505,0.0001116583,0.0003273487],"domain_scores_gemma":[0.9998105,0.000009272178,0.000005059331,0.00009382762,0.0000204297,0.00006090838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001517815,0.00000176834,0.00002191086,0.0001931864,0.000003736478,0.000006805474,0.00596477,0.3051419,0.6851468,0.00002554665,0.000001037927,0.003490991],"study_design_scores_gemma":[0.00008783317,0.000004497479,0.00122719,0.0002025075,0.00000426171,0.00001137143,0.00007740466,0.8510696,0.1471373,0.00003226476,0.00002130385,0.000124374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616281,0.0001789217,0.03756119,0.0000392358,0.0004023064,0.00006158363,0.000001352242,0.00008156065,0.00004577287],"genre_scores_gemma":[0.9975458,0.00005741178,0.002313192,0.00002761842,0.00003916811,0.000003434001,0.00000115862,0.00000877745,0.000003461334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5459278,"threshold_uncertainty_score":0.4435957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004663100996875976,"score_gpt":0.1860196565729836,"score_spread":0.1813565555761076,"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."}}