{"id":"W4403384524","doi":"10.1186/s40069-024-00703-x","title":"Machine Learning Approach to Rapidly Evaluate Curling of Concrete Pavement","year":2024,"lang":"en","type":"article","venue":"International Journal of Concrete Structures and Materials","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; Ministry of Education; National Research Foundation","keywords":"Curling; Structural material; Solid mechanics; Engineering; Forensic engineering; Construction engineering; Civil engineering; Materials science; Mechanical engineering; Composite material","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":[0.0009175813,0.0009866111,0.0006492587,0.001238008,0.0002176753,0.0005709224,0.0008095545,0.0008482384,0.001179697],"category_scores_gemma":[0.003096143,0.0002462385,0.0005152399,0.0006143422,0.0002758371,0.0005579503,0.0004628038,0.0007803746,0.0005087327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000533064,"about_ca_system_score_gemma":0.0006514746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004275717,"about_ca_topic_score_gemma":0.003409815,"domain_scores_codex":[0.999539,0.00008383011,0.00003498954,0.0001455197,0.0001402307,0.00005636762],"domain_scores_gemma":[0.9988167,0.0005515615,0.0001902259,0.00008843085,0.000308408,0.00004467035],"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":[0.0001667046,0.000213381,0.006635044,0.00007579979,0.0000796177,0.00008084012,0.00003889801,0.8249819,0.01250187,0.0008805958,0.0008540165,0.1534912],"study_design_scores_gemma":[0.000001320692,0.00002409095,0.0006252042,0.000002618136,0.000003132148,0.000006479529,0.000003513451,0.9975755,0.001474212,0.0001994495,0.00008081146,0.000003709843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1445476,0.000397482,0.8499223,0.0001212803,0.0000600989,0.00009991796,0.0002699981,0.002594941,0.001986419],"genre_scores_gemma":[0.919382,0.00009612829,0.07872505,0.0000563516,0.00002487951,0.0001217892,0.0003607073,0.00004290579,0.001190185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004275717,"threshold_uncertainty_score":0.008501649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009751637135203777,"score_gpt":0.2598701819463539,"score_spread":0.2501185448111501,"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."}}