{"id":"W1987501297","doi":"10.1016/j.ijsolstr.2014.03.009","title":"Multiscale micromechanical modeling of the constitutive response of carbon nanotube-reinforced structural adhesives","year":2014,"lang":"en","type":"article","venue":"International Journal of Solids and Structures","topic":"Carbon Nanotubes in Composites","field":"Materials Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Representative elementary volume; Finite element method; Composite material; Nonlinear system; Constitutive equation; Carbon nanotube; Adhesive; Micromechanics; Modulus; Ultimate tensile strength; Linear elasticity; Composite number; Structural engineering; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000380215,0.0001137957,0.0002765087,0.0001116993,0.00004312799,0.00003538835,0.0005255083,0.00006626628,0.00002503809],"category_scores_gemma":[0.0004884091,0.0000717912,0.0001277178,0.00004722629,0.0003328911,0.0001034837,0.0001711514,0.0001304821,8.838131e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003222197,"about_ca_system_score_gemma":0.00008414617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006766646,"about_ca_topic_score_gemma":0.000004448477,"domain_scores_codex":[0.9985806,0.0001392592,0.0005773854,0.0001092598,0.0004860562,0.0001073871],"domain_scores_gemma":[0.9985232,0.000316423,0.0004895141,0.0001299335,0.0004956799,0.00004526498],"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":[0.0009202448,0.000004098466,0.0005929458,0.000009636301,0.00004496445,0.00000287439,0.0005134404,0.01010512,0.9815395,0.00603828,0.000003974282,0.0002249642],"study_design_scores_gemma":[0.0008033015,0.0001436662,0.002990466,0.0001436415,0.00002913676,0.0002555793,0.0001042751,0.03774311,0.9509338,0.006753883,0.00001144739,0.00008772653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975086,0.0001495308,0.0007539886,0.0001521224,0.001265598,0.00005717158,0.0000314253,0.000003489463,0.00007804888],"genre_scores_gemma":[0.9975419,0.00001140069,0.002233648,0.00004825863,0.0001479308,4.094209e-7,4.164479e-7,0.000006033896,0.00001001499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03060569,"threshold_uncertainty_score":0.292756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009279636151213008,"score_gpt":0.2604672439332643,"score_spread":0.2511876077820513,"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."}}