{"id":"W7133081641","doi":"","title":"A parallel adaptive-mesh refinement scheme for predicting laminar diffusion flames","year":2004,"lang":"","type":"dissertation","venue":"TSpace","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bibliographical Society of Canada; University of Toronto","funders":"University of Toronto","keywords":"Laminar flow; Solver; Discretization; Inviscid flow; Quadrilateral; Adaptive mesh refinement; Rotational symmetry; Convection–diffusion equation; Piecewise; Smoothing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000492582,0.0004515761,0.0005861229,0.0003297907,0.0004139312,0.0003728323,0.0009805005,0.000622757,0.001291247],"category_scores_gemma":[0.001147276,0.0002823537,0.0003922554,0.0004155992,0.0002722345,0.0004460312,0.0005269242,0.0005827656,0.0003062676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003635304,"about_ca_system_score_gemma":0.0007553142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006194871,"about_ca_topic_score_gemma":0.004081709,"domain_scores_codex":[0.9998432,0.00004098209,0.000008040345,0.00001875871,0.0000758777,0.00001312537],"domain_scores_gemma":[0.9997483,0.00008421396,0.00002236397,0.00004243304,0.00008542365,0.00001722883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001369908,0.00005668272,0.001581852,0.00007619975,0.00002470251,0.00009613774,0.00005250885,0.8796239,0.02878472,0.005388238,0.001038269,0.08313974],"study_design_scores_gemma":[0.000007712358,0.00001031779,0.00009266988,0.000001866786,0.000001748445,0.00000911745,0.000002203878,0.9977753,0.001254724,0.0003084695,0.0005329433,0.000002897383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05505944,0.0002565776,0.9397353,0.0001219995,0.00008221215,0.0001348287,0.0001701595,0.001138079,0.003301413],"genre_scores_gemma":[0.3568607,0.000246282,0.6392754,0.00004256993,0.00002608437,0.0002285881,0.0003237605,0.0001771387,0.002819554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006194871,"threshold_uncertainty_score":0.0123176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284005989651436,"score_gpt":0.2776634081073217,"score_spread":0.2648233482108073,"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."}}