{"id":"W2885713309","doi":"10.1139/cjce-2018-0051","title":"Integrated predictive artificial neural network fatigue endurance limit model for asphalt concrete pavements","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Asphalt Pavement Performance Evaluation","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Asphalt; Structural engineering; Stiffness; Artificial neural network; Fatigue limit; Asphalt concrete; Limit (mathematics); Reliability (semiconductor); Engineering; Mathematics; Computer science; Materials science; Machine learning; Mathematical analysis; Composite material","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004281569,0.000232362,0.0002720024,0.0002455649,0.0001218214,0.00006216192,0.0002379165,0.0001044661,0.00009278861],"category_scores_gemma":[0.0001209767,0.0002498378,0.00009566963,0.0002690895,0.00004515114,0.0004856202,0.000006762893,0.0002902316,0.000004934087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811207,"about_ca_system_score_gemma":0.0003240549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003413452,"about_ca_topic_score_gemma":0.009995344,"domain_scores_codex":[0.9984612,0.0000128063,0.0005800202,0.0001279475,0.0002172352,0.0006007815],"domain_scores_gemma":[0.9988819,0.00005187599,0.0001264329,0.000140987,0.0003724086,0.0004263706],"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":[0.00001705718,9.055518e-7,0.0002826969,0.00003913128,0.00009256738,0.000005810823,0.00045993,0.9930803,0.001745643,0.00009480923,0.003156866,0.001024309],"study_design_scores_gemma":[0.0004096775,0.0001591026,0.0005204342,0.0001882308,0.00004953348,0.00001245524,0.0000370215,0.9952021,0.001619117,0.0001062915,0.001475639,0.0002204613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2229215,0.0004853032,0.772689,0.00004775103,0.002894522,0.0003715136,0.00006339376,0.00007845088,0.0004486055],"genre_scores_gemma":[0.9956649,0.0000139066,0.002676039,0.00006290211,0.00144274,0.00002526513,0.00001832674,0.00006392817,0.00003199378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7727434,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03547569699291268,"score_gpt":0.2361622862447215,"score_spread":0.2006865892518089,"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."}}