{"id":"W4283791591","doi":"10.3390/infrastructures7070091","title":"Predicting Pavement Condition Index Using Fuzzy Logic Technique","year":2022,"lang":"en","type":"article","venue":"Infrastructures","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Fuzzy logic; Rut; Pavement management; Defuzzification; Consistency (knowledge bases); Fuzzy set; Mean squared error; Mathematics; Data mining; Statistics; Computer science; Engineering; Fuzzy number; Artificial intelligence; Civil engineering; Geography; Asphalt","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001738413,0.0002251592,0.0001815703,0.0001920318,0.0004466538,0.00004363917,0.0002254209,0.0000790078,0.0004309595],"category_scores_gemma":[0.0000171316,0.0002289546,0.00006580162,0.0002351073,0.00004117491,0.0001334267,0.0001519144,0.0006453593,0.000001672501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003765125,"about_ca_system_score_gemma":0.00002913641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006361956,"about_ca_topic_score_gemma":0.000003557857,"domain_scores_codex":[0.9987423,0.00004640134,0.0002830232,0.0002121999,0.0003148763,0.0004011586],"domain_scores_gemma":[0.9995826,0.00002305324,0.00007152676,0.0002287395,0.00003386527,0.00006018047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001054173,0.000005869621,0.01662462,0.00005909286,0.00005511815,0.00003511531,0.0004871756,0.8275009,0.150388,0.001152533,0.001119343,0.002561651],"study_design_scores_gemma":[0.003066864,0.0006072157,0.1784477,0.0002718123,0.0002160506,0.001838678,0.007032365,0.1795264,0.3331389,0.2522981,0.03991219,0.003643791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9436381,0.0001998703,0.04781802,0.00001464362,0.00264909,0.0005178021,0.00008689532,0.0007704748,0.004305036],"genre_scores_gemma":[0.9930751,0.000004070716,0.006095955,0.0001392178,0.0004511562,0.0001388633,0.0000324657,0.00004924747,0.00001394664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6479746,"threshold_uncertainty_score":0.9336497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008155188034249816,"score_gpt":0.2335592612324246,"score_spread":0.2254040731981748,"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."}}