{"id":"W4386762410","doi":"10.1139/cjce-2023-0046","title":"Utilizing different artificial intelligence techniques for efficient condition assessment of building components","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Facilities and Workplace Management","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"HVAC; Facility management; Schedule; Computer science; Visual inspection; Process (computing); Service (business); Roof; Building automation; Artificial intelligence; Reliability engineering; Engineering; Air conditioning; Civil engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004538932,0.0001129186,0.0002238952,0.0005806814,0.0000616939,0.00002857357,0.0001682842,0.00004916271,0.0001710534],"category_scores_gemma":[0.00004531529,0.0001135402,0.000113353,0.0002008249,0.00002768639,0.00003055827,0.00001538661,0.0001429588,0.000001745142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849068,"about_ca_system_score_gemma":0.00005821428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002944941,"about_ca_topic_score_gemma":0.002210922,"domain_scores_codex":[0.9989596,0.00001901804,0.0004519272,0.0001066025,0.0001402747,0.0003225533],"domain_scores_gemma":[0.9993516,0.0001222824,0.0001450356,0.0001182082,0.00008265743,0.0001801865],"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":[0.0000649567,0.0001369724,0.003287633,0.000738494,0.0006108024,0.0002256752,0.00328831,0.5985091,0.01644791,0.3400976,0.007055339,0.0295372],"study_design_scores_gemma":[0.001133675,0.001732167,0.06732635,0.003733784,0.0003543608,0.0001233262,0.01383537,0.8345492,0.01996377,0.007422626,0.04827155,0.001553816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5193704,0.0001542236,0.476366,0.0003937618,0.002200051,0.0003601539,0.0000628018,0.000047658,0.001044894],"genre_scores_gemma":[0.9990441,0.000004645538,0.0007424095,0.0000135148,0.0001053982,0.00001826053,0.000008082788,0.00001738125,0.00004624881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4796736,"threshold_uncertainty_score":0.4630034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04059435685627685,"score_gpt":0.3097786676508982,"score_spread":0.2691843107946214,"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."}}