{"id":"W2344356479","doi":"","title":"Quality Function Deployment Based Method for Condition Assessment of Concrete Bridges","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quality function deployment; Visual inspection; Bridge (graph theory); Serviceability (structure); Engineering; Quality assurance; Nondestructive testing; Bridge maintenance; Construction engineering; Computer science; Reliability engineering; Civil engineering; Operations management; Artificial intelligence; Structural engineering; External quality assessment","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0579397,0.000456911,0.001164612,0.002679895,0.00111118,0.0002299107,0.00129044,0.0004071838,0.001231982],"category_scores_gemma":[0.004904142,0.0003360411,0.000849825,0.00369942,0.001130927,0.001237524,0.00001970994,0.0008299485,0.00008353979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003623102,"about_ca_system_score_gemma":0.0013586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005635093,"about_ca_topic_score_gemma":0.008699923,"domain_scores_codex":[0.9741735,0.006145342,0.00367691,0.001936251,0.01243209,0.001635949],"domain_scores_gemma":[0.9600902,0.0210116,0.0009826323,0.001259645,0.0159071,0.0007487909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01164043,0.0007145089,0.5321769,0.0008711496,0.0007543594,0.00005369117,0.003871849,0.0100824,0.1461285,0.1182455,0.02378106,0.1516797],"study_design_scores_gemma":[0.004602025,0.001698314,0.9152631,0.000235511,0.0001088967,1.516989e-7,0.006688616,0.005780693,0.01424825,0.03012701,0.02071084,0.0005366157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4254129,0.00006370621,0.5590016,0.009806595,0.0002073352,0.002173057,0.002647677,0.0001222602,0.0005648299],"genre_scores_gemma":[0.957309,0.0002194486,0.03909145,0.0001290549,0.0001675188,0.0009832456,0.000557246,0.00007477863,0.001468231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5318961,"threshold_uncertainty_score":0.9999092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2005256787913327,"score_gpt":0.5341348019206298,"score_spread":0.3336091231292971,"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."}}