{"id":"W3178302455","doi":"10.1108/ec-02-2021-0096","title":"An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling","year":2021,"lang":"en","type":"article","venue":"Engineering Computations","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Generalizability theory; Benchmark (surveying); Machine learning; Computer science; Reinforcement learning; Artificial intelligence; Probabilistic logic; Scalability; Engineering design process; Process (computing); Variety (cybernetics); Pipeline (software); Industrial engineering; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001889893,0.000245652,0.0002852913,0.0002603176,0.0001083566,0.00005240008,0.00008250028,0.0001439048,0.000002503321],"category_scores_gemma":[0.0002781831,0.0003084778,0.00005636171,0.0004745828,0.00001029988,0.0003533757,0.00002652054,0.0003380001,7.174713e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003629612,"about_ca_system_score_gemma":0.0000320525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002349674,"about_ca_topic_score_gemma":0.000003985686,"domain_scores_codex":[0.9987104,0.00001345215,0.0004630665,0.0002648545,0.0001240834,0.000424173],"domain_scores_gemma":[0.9990814,0.0003445453,0.00002804063,0.0002588165,0.0001701505,0.0001170632],"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":[0.000001808042,0.000001021262,0.000009845166,0.0007367338,0.00004059969,0.0001368501,0.001730877,0.9932982,0.0006000122,0.003169616,0.00001951528,0.0002549028],"study_design_scores_gemma":[0.0002256465,0.00003908048,0.00001675336,0.0004704939,0.00001527859,0.0004043452,0.0005036986,0.9962971,0.001133064,0.0005364664,0.00009051269,0.0002675296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4173947,0.0004677821,0.5794432,0.00001376051,0.00089726,0.0003232861,0.00001636685,0.001437912,0.000005836137],"genre_scores_gemma":[0.7124217,0.00006292375,0.2868972,0.00000550464,0.0002035159,0.0003053477,0.00003415787,0.00006532573,0.00000430802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.295027,"threshold_uncertainty_score":0.9999368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04201728188945038,"score_gpt":0.3242465077013955,"score_spread":0.2822292258119452,"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."}}