{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004414198,0.00237826,0.001591131,0.002627823,0.000397583,0.001458603,0.003762853,0.001516199,0.003804113],"category_scores_gemma":[0.005089459,0.0005602799,0.000705617,0.002320298,0.001256931,0.002328305,0.0009335157,0.0008586801,0.002296664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006949946,"about_ca_system_score_gemma":0.0007154802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003345141,"about_ca_topic_score_gemma":0.003729562,"domain_scores_codex":[0.9965332,0.0006854825,0.0001542422,0.000511482,0.001969558,0.0001461683],"domain_scores_gemma":[0.9938792,0.002429518,0.0005811175,0.000530289,0.002316351,0.0002634272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006417079,0.0008429494,0.04861951,0.0007431189,0.00008202182,0.00008542367,0.0002022012,0.02291348,0.1197467,0.001907864,0.002657001,0.8015581],"study_design_scores_gemma":[0.00006069243,0.003253534,0.04328629,0.0002972331,0.000233209,0.0007270972,0.0006255053,0.524246,0.3917622,0.008230523,0.02703915,0.0002386031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.296374,0.02860309,0.6514161,0.0009761697,0.0004321135,0.0002558819,0.001020387,0.006064232,0.01485799],"genre_scores_gemma":[0.8860367,0.01021906,0.09259773,0.0002154849,0.0003448717,0.0001091952,0.001268558,0.0003367963,0.008871648],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004414198,"threshold_uncertainty_score":0.02334481,"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."}}