{"id":"W3103111311","doi":"10.11159/iccste20.238","title":"Probabilistic Inference Approach for Predicting Concrete CompressiveStrength - A Bayesian Network Algorithm","year":2020,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Bayesian network; Inference; Probabilistic logic; Algorithm; Bayesian inference; Bayesian probability; Compressive strength; Artificial intelligence; Machine learning; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003190397,0.0007715366,0.001192716,0.001599992,0.0005607808,0.00106762,0.001715849,0.00136483,0.002045469],"category_scores_gemma":[0.007903533,0.0006991236,0.0008033048,0.0009899187,0.0006870221,0.001650466,0.0008524801,0.001746664,0.0005252606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001205285,"about_ca_system_score_gemma":0.001623006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01255776,"about_ca_topic_score_gemma":0.008751901,"domain_scores_codex":[0.9989221,0.0003917683,0.00007271478,0.0002401972,0.0002958254,0.00007735548],"domain_scores_gemma":[0.9975957,0.001722494,0.0001823322,0.00006702044,0.000386777,0.00004574335],"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":[0.00008561726,0.00005860742,0.00172973,0.00005597417,0.00006004837,0.00003077624,0.0000454666,0.9010342,0.0007788662,0.01096994,0.0005568828,0.08459391],"study_design_scores_gemma":[0.000004925177,0.000008998041,0.0001160402,0.000005978169,0.000005275434,0.000006008955,0.000002551822,0.996954,0.0001426344,0.00260039,0.0001490885,0.000004065878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006735553,0.0001891147,0.9917606,0.0001288864,0.00001723883,0.00003989337,0.00004764626,0.0001899427,0.0008911121],"genre_scores_gemma":[0.4298401,0.0008407125,0.5649132,0.0002038563,0.0001263855,0.0004519533,0.0004340088,0.00008112684,0.003108593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01255776,"threshold_uncertainty_score":0.02496934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01232264888484142,"score_gpt":0.2115356807746146,"score_spread":0.1992130318897732,"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."}}