{"id":"W2938130030","doi":"10.1016/j.matchar.2019.109929","title":"Microstructural and mechanical characterization of variability in porous advanced ceramics using X-ray computed tomography and digital image correlation","year":2019,"lang":"en","type":"article","venue":"Materials Characterization","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Army Research Laboratory","keywords":"Materials science; Microstructure; Digital image correlation; Porosity; Composite material; Tomography; Compressive strength; Characterization (materials science); Ceramic; Compression (physics); Porous medium; Optics; Nanotechnology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001689246,0.0001763014,0.0002967041,0.0001238081,0.00003039579,0.0001081037,0.00004759646,0.00009789945,0.00002014391],"category_scores_gemma":[0.00002339009,0.0001941225,0.00001552122,0.0001834141,0.0000376311,0.001178752,0.000042268,0.00005734673,0.000001140064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003692706,"about_ca_system_score_gemma":0.000006616983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002171176,"about_ca_topic_score_gemma":2.150727e-7,"domain_scores_codex":[0.9990257,0.00005859214,0.0004358716,0.0002289538,0.00008464051,0.0001662218],"domain_scores_gemma":[0.999604,0.00003363645,0.0001482541,0.0001273906,0.00005254379,0.00003421026],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005197513,0.00001381793,0.007765869,0.0001888338,0.000007116168,6.654721e-7,0.000211038,0.0004980767,0.9873686,0.00008716273,1.415192e-8,0.003806797],"study_design_scores_gemma":[0.0007675541,0.00003462056,0.4614221,0.0001423705,0.00001434194,0.00001596363,0.00003362515,0.1349757,0.4020947,0.0001841147,0.0000103723,0.0003045267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9156529,0.000004956452,0.08320001,0.000006796261,0.0004730848,0.0003669576,0.0002072267,0.00008207169,0.000006043777],"genre_scores_gemma":[0.9967931,0.00002108868,0.001886496,0.00001197936,0.0000342928,0.000004378583,0.001215261,0.00003106959,0.000002339895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5852739,"threshold_uncertainty_score":0.7916085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003063504800080359,"score_gpt":0.1846294989161861,"score_spread":0.1815659941161058,"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."}}