{"id":"W2974152154","doi":"10.1080/17515831.2019.1666232","title":"Texture shape effects on hydrodynamic journal bearing performances using mass-conserving numerical approach","year":2019,"lang":"en","type":"article","venue":"Tribology - Materials Surfaces & Interfaces","topic":"Tribology and Lubrication Engineering","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Wedge (geometry); Reynolds equation; Mechanics; Cavitation; Fluid bearing; Texture (cosmology); Bearing (navigation); Eccentricity (behavior); Surface finish; Materials science; Lubrication; Reynolds number; Mathematics; Geometry; Computer science; Physics; Image (mathematics); Composite material; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005211307,0.0004807416,0.0007556752,0.0002887946,0.0001854797,0.0001598783,0.0005391695,0.0003899031,0.0006368707],"category_scores_gemma":[0.00004706138,0.0004317129,0.00008526016,0.0002383753,0.00007712177,0.0004400891,0.00008476505,0.0006513575,0.0003615404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001465378,"about_ca_system_score_gemma":0.00002854518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001148459,"about_ca_topic_score_gemma":7.42629e-7,"domain_scores_codex":[0.9978459,0.0001889218,0.0006181067,0.0004237173,0.0002058663,0.0007174709],"domain_scores_gemma":[0.9990442,0.0002694213,0.0001777537,0.000346625,0.00004957393,0.0001124517],"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":[0.00008155172,0.00002361227,0.002593811,0.000293013,0.000161221,0.00001033371,0.0002742001,0.3952014,0.6006818,0.0000529537,0.00006204276,0.000564072],"study_design_scores_gemma":[0.001142434,0.0003108608,0.007974899,0.0002905847,0.00009673108,0.0004146487,0.0002431224,0.5549622,0.4330828,0.0001123995,0.0005263893,0.0008428554],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896978,0.00118228,0.003962231,0.00005467715,0.003550943,0.0003493404,0.00001534509,0.0004654848,0.0007219403],"genre_scores_gemma":[0.9966041,0.0001226992,0.002765485,0.00006092829,0.0002315295,0.00001874106,0.00002073639,0.00008544267,0.00009032128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.167599,"threshold_uncertainty_score":0.9998134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007958548315036148,"score_gpt":0.2163369550503739,"score_spread":0.2083784067353378,"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."}}