{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004418441,0.0001965972,0.0001922527,0.00003284913,0.00006684297,0.00006078329,0.0002652416,0.00005908944,0.000007910893],"category_scores_gemma":[0.0000518894,0.0001553372,0.00006277404,0.0001068683,0.00003998408,0.0002007096,0.00001291197,0.0002079005,7.695802e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002229764,"about_ca_system_score_gemma":0.00001226779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004604401,"about_ca_topic_score_gemma":0.00000116219,"domain_scores_codex":[0.9991083,0.00000124868,0.0002856831,0.000204466,0.0002032943,0.0001970793],"domain_scores_gemma":[0.9995645,0.00003831057,0.00009699239,0.00003866805,0.0001886313,0.00007292748],"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":[0.0001276946,0.00000436696,0.01134251,0.00150976,0.0003437257,0.000001044383,0.003372995,0.82011,0.04211614,0.1138763,0.0001664583,0.007028989],"study_design_scores_gemma":[0.0003493338,0.00004626913,0.009592405,0.0001858632,0.00002992817,0.000002225344,0.000156487,0.984083,0.004271479,0.001036646,0.00006648176,0.0001798564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9201274,0.00007120795,0.0754176,0.0003453463,0.001031,0.0008428791,0.0002527702,0.0003322165,0.001579603],"genre_scores_gemma":[0.9845905,0.00001606432,0.01494568,0.00003145205,0.0003106943,0.00004762643,0.00003081256,0.00002190505,0.000005283685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.163973,"threshold_uncertainty_score":0.6334465,"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."}}