{"id":"W2090207103","doi":"10.1016/j.commatsci.2010.09.031","title":"Modeling of grain boundary character reconstruction and predicting intergranular fracture susceptibility of textured and random polycrystalline materials","year":2010,"lang":"en","type":"article","venue":"Computational Materials Science","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; McGill University","funders":"","keywords":"Materials science; Grain boundary; Fracture (geology); Intergranular fracture; Monte Carlo method; Texture (cosmology); Grain boundary strengthening; Intergranular corrosion; Stress (linguistics); Crystallite; Voronoi diagram; Microstructure; Metallurgy; Geometry; Composite material; Mathematics; Artificial intelligence; Computer science; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002007814,0.0002921807,0.0004192073,0.0002509266,0.0003207337,0.0005015685,0.0009379444,0.0008777831,0.000655847],"category_scores_gemma":[0.001104735,0.0003491216,0.000293003,0.0003319131,0.0004940865,0.0005446954,0.0001949287,0.0003580811,0.00005596428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007972426,"about_ca_system_score_gemma":0.0007897907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329372,"about_ca_topic_score_gemma":0.01260136,"domain_scores_codex":[0.9999338,0.0000106854,0.000003453983,0.00001824042,0.00001945557,0.00001439931],"domain_scores_gemma":[0.9995153,0.0003056554,0.00006307495,0.00004124235,0.00005250219,0.00002222923],"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":[0.00002124158,0.00002152229,0.0008246807,0.00001215814,0.000005164393,0.00003064148,0.00001165066,0.9939248,0.003106219,0.0008456415,0.00005470943,0.001141686],"study_design_scores_gemma":[0.000001713963,0.000002831507,0.0001656155,2.852799e-7,7.789831e-7,0.000002359378,0.000001919458,0.999341,0.0003553206,0.0001175326,0.00000992729,8.155268e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9630967,0.0001199965,0.03429263,0.0001071746,0.00000852875,0.00001733735,0.0001478084,0.0001460978,0.002063827],"genre_scores_gemma":[0.9944511,0.00002992123,0.005122907,0.000006587104,0.000002351952,0.00001219572,0.00006644953,0.00002140226,0.0002871177],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01329372,"threshold_uncertainty_score":0.02643269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005606269350909682,"score_gpt":0.2100583492312649,"score_spread":0.2044520798803552,"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."}}