{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000357626,0.0001383704,0.0001633793,0.0009542769,0.0002228209,0.0005901125,0.0003313083,0.0002444662,0.0006269451],"category_scores_gemma":[0.00100411,0.0002303547,0.0001438623,0.0006674554,0.000713473,0.0004114261,0.0002990125,0.0002216143,0.00005797859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002958409,"about_ca_system_score_gemma":0.0003265236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002044495,"about_ca_topic_score_gemma":0.003652263,"domain_scores_codex":[0.9997273,0.00001740887,0.00001968531,0.00007765683,0.0001344428,0.0000235228],"domain_scores_gemma":[0.9991556,0.0002351575,0.0002429329,0.00008728138,0.0002422638,0.00003667865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004372161,0.00005219301,0.0336289,0.000103167,0.00003881408,0.0001685257,0.0002901912,0.004624247,0.9469953,0.0005969255,0.0001097237,0.01295474],"study_design_scores_gemma":[0.00002882508,0.0003526686,0.5027089,0.00001689213,0.00009278186,0.001324958,0.0005316997,0.05714034,0.4352639,0.0005117619,0.001963186,0.00006411459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885892,0.0001898633,0.01038457,0.00001336107,0.000004129831,0.00001448497,0.000177336,0.00008278977,0.0005443834],"genre_scores_gemma":[0.9977313,0.0000267624,0.002012022,0.000004335095,0.00000226622,0.000006251169,0.00007310927,0.000009324453,0.0001347739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002044495,"threshold_uncertainty_score":0.004065216,"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."}}