{"id":"W7012008128","doi":"","title":"Numerical modeling of SLD secondary droplet flows for in-flight icing","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Work (physics); Numerical modeling; Constructive; Icing; Numerical models; Mathematical model","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003912747,0.0006172069,0.0006483244,0.0005003402,0.0007427718,0.00129413,0.0009015747,0.001442723,0.002997073],"category_scores_gemma":[0.001272804,0.0003549248,0.0007795919,0.0003247983,0.0006749308,0.0008174669,0.0009194162,0.0009677546,0.0004173812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060109,"about_ca_system_score_gemma":0.001256218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01546118,"about_ca_topic_score_gemma":0.006779415,"domain_scores_codex":[0.9998593,0.00002786759,0.000007952342,0.00001903359,0.00005242768,0.00003331213],"domain_scores_gemma":[0.9996622,0.0001592038,0.00002983376,0.00001883989,0.00008524231,0.00004475602],"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":[0.00005590723,0.00005553186,0.001162728,0.00006360181,0.00001476898,0.000184539,0.00007193701,0.9799879,0.00607714,0.004411817,0.001443032,0.006471025],"study_design_scores_gemma":[0.000006609532,0.00001137119,0.0001462804,0.00000415755,0.000001602573,0.00001146777,0.00001073392,0.9985633,0.0004547561,0.000262808,0.0005222724,0.000004560874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4330933,0.003059768,0.4748864,0.003199633,0.001519288,0.0004297626,0.001154861,0.001014482,0.08164243],"genre_scores_gemma":[0.9573308,0.0007560014,0.02678109,0.0001391606,0.000105121,0.00013271,0.0002731741,0.0001412656,0.01434068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01546118,"threshold_uncertainty_score":0.03074235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01932161441319482,"score_gpt":0.2662632130443343,"score_spread":0.2469415986311394,"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."}}