{"id":"W4391265677","doi":"10.54097/9qknfc57","title":"Artificial Intelligence and Applications in Structural and Material Engineering","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Engineering","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":[0.00106439,0.0009132009,0.0007547131,0.001773932,0.0004724881,0.002353714,0.0009702695,0.001999154,0.006605389],"category_scores_gemma":[0.002921812,0.0002792983,0.0007359336,0.003007266,0.001652032,0.002025003,0.001393838,0.002690573,0.002162763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008469744,"about_ca_system_score_gemma":0.0008302107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009839104,"about_ca_topic_score_gemma":0.0007205633,"domain_scores_codex":[0.9990194,0.0002582695,0.00008045002,0.0001657694,0.0004250655,0.00005104569],"domain_scores_gemma":[0.9984487,0.0009631875,0.0001042851,0.0001628912,0.0002653758,0.00005545037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004934172,0.0000983948,0.002287736,0.002180438,0.0001236658,0.0004306587,0.0002693061,0.04107315,0.004339816,0.4143326,0.03387739,0.5009376],"study_design_scores_gemma":[0.00001292275,0.00008749912,0.002011191,0.0009749854,0.00004121656,0.0006623536,0.0001925551,0.09451323,0.002669034,0.5890694,0.3097042,0.00006144003],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01190264,0.2817978,0.4843816,0.02349083,0.00333233,0.0001717662,0.0005800144,0.001185515,0.1931574],"genre_scores_gemma":[0.3657552,0.2818801,0.2921615,0.00401296,0.005134343,0.000356622,0.0009713684,0.0002950242,0.04943292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006605389,"threshold_uncertainty_score":0.02209723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006223425637294456,"score_gpt":0.2154601933061867,"score_spread":0.2092367676688923,"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."}}