{"id":"W596634955","doi":"10.1016/j.cemconres.2015.05.013","title":"Service life prediction and performance testing — Current developments and practical applications","year":2015,"lang":"en","type":"article","venue":"Cement and Concrete Research","topic":"Concrete Corrosion and Durability","field":"Engineering","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Durability; Performance prediction; Service life; Computer science; Reliability engineering; Service (business); Performance indicator; Predictive modelling; Current (fluid); Engineering; Simulation; Machine learning; Database","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":[],"consensus_categories":[],"category_scores_codex":[0.0008191939,0.00008286126,0.00008884889,0.00006093822,0.0001610897,0.00008231835,0.00003636097,0.00003977692,0.000008373542],"category_scores_gemma":[0.0001886129,0.00007841422,0.000004298568,0.0002191559,0.00007367568,0.0001581815,0.0001490979,0.0002375621,0.00001077008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000465447,"about_ca_system_score_gemma":0.00009694404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001038064,"about_ca_topic_score_gemma":0.000001982126,"domain_scores_codex":[0.9991176,0.00004627906,0.0001512887,0.0001940068,0.0002723849,0.0002184216],"domain_scores_gemma":[0.9991487,0.0001710417,0.0000123132,0.0001093208,0.0002387831,0.0003198716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001929081,0.00001590538,0.3778583,0.001896601,0.00005071288,0.000003536225,0.002752582,0.00003215047,0.00995167,0.0007047052,0.003455599,0.6030853],"study_design_scores_gemma":[0.002957675,0.000478078,0.1803804,0.0002800722,0.00004385709,0.0000572799,0.002886446,0.6232373,0.001397238,0.0002440931,0.1874131,0.0006244926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995499,0.001619891,0.0006148648,0.0002583109,0.00005643088,0.000394483,0.000005048561,0.00007765582,0.001474325],"genre_scores_gemma":[0.9981353,0.0009600347,0.00064628,0.00002858357,0.00005921712,0.000133562,0.000009583848,0.000008728205,0.00001876087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6232051,"threshold_uncertainty_score":0.3197639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2687046645629043,"score_gpt":0.3742316640180082,"score_spread":0.1055269994551039,"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."}}