{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004287783,0.0006645673,0.0004929474,0.0003655712,0.0002727554,0.0006628462,0.001038614,0.0008827043,0.001696565],"category_scores_gemma":[0.0007953558,0.0003745633,0.000585425,0.0003483714,0.0002090421,0.000484591,0.000368441,0.0008114939,0.0003176263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006969825,"about_ca_system_score_gemma":0.000743371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02463455,"about_ca_topic_score_gemma":0.02002499,"domain_scores_codex":[0.9997888,0.00003684659,0.00001566137,0.00006788127,0.00005932563,0.00003153171],"domain_scores_gemma":[0.9996904,0.0001291203,0.00003499597,0.00001248606,0.0001257559,0.000007211463],"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":[0.0000235635,0.00002309472,0.0006610422,0.00001884616,0.00001434281,0.00002585208,0.0000108544,0.9914921,0.0005853335,0.0002014975,0.000136459,0.006807081],"study_design_scores_gemma":[9.074142e-7,0.000006532336,0.0001441102,0.000001284312,0.00000250329,0.000001510317,0.000001418879,0.9996239,0.0001211201,0.00005136429,0.00004402316,0.000001265423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3448039,0.0008738053,0.6334216,0.0003433276,0.0001364075,0.0001763125,0.0008248875,0.001464234,0.01795551],"genre_scores_gemma":[0.9746698,0.0002150127,0.01705541,0.00003688635,0.00001530564,0.000231881,0.0004907703,0.00002104083,0.007263782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02463455,"threshold_uncertainty_score":0.04898232,"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."}}