{"id":"W3102697381","doi":"10.1007/s42452-020-03778-9","title":"Forecasting the deterioration of cement-based mixtures under sulfuric acid attack using support vector regression based on Bayesian optimization","year":2020,"lang":"en","type":"article","venue":"SN Applied Sciences","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Energy; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Bayesian probability; Sulfuric acid; Computer science; Machine learning; Regression analysis; Test data; Artificial intelligence; Materials science; Metallurgy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005493182,0.0006811257,0.0007867778,0.0006746034,0.0001611398,0.0005155099,0.0004535431,0.0007896966,0.0003017483],"category_scores_gemma":[0.001293596,0.0003062627,0.0005843705,0.0004743435,0.0002572886,0.0006901901,0.0002364749,0.000704964,0.00009743087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000477473,"about_ca_system_score_gemma":0.0005202271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01120019,"about_ca_topic_score_gemma":0.008091456,"domain_scores_codex":[0.9998294,0.00002935307,0.00001441483,0.00004222503,0.00005792431,0.00002660343],"domain_scores_gemma":[0.9994608,0.0002484543,0.0001232733,0.00001820945,0.0001206268,0.00002870409],"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":[0.0003179982,0.0001036705,0.009247014,0.00005956342,0.00004823984,0.00005123241,0.00002054293,0.9633071,0.009052003,0.0003090995,0.0002235948,0.01725992],"study_design_scores_gemma":[0.000001754888,0.00001276004,0.0008491399,5.361404e-7,0.000002767928,0.000001523243,0.000001600572,0.9984654,0.000602058,0.00004927023,0.00001107993,0.000002032571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9267797,0.0003756707,0.07163168,0.0001333026,0.00003826898,0.00002067187,0.00019052,0.0002608838,0.0005693994],"genre_scores_gemma":[0.9959612,0.00008653761,0.003564511,0.000005577521,0.000008020341,0.000005642627,0.0001146895,0.000004238968,0.0002494503],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01120019,"threshold_uncertainty_score":0.02227002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04647309791304687,"score_gpt":0.259764197942392,"score_spread":0.2132911000293451,"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."}}