{"id":"W2610398826","doi":"","title":"Developing Cost-Effective Pavement Maintenance and Rehabilitation Schedules: Application of MEPDG-Based Distress Models and Key Performance Index","year":2017,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Index (typography); Rehabilitation; Distress; Engineering; Computer science; Psychology; Medicine; Physical therapy; Computer security; Clinical psychology; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001128011,0.0001999007,0.000310481,0.0001559284,0.0001512935,0.00001575014,0.0001491004,0.0001772708,0.000001355265],"category_scores_gemma":[0.00001182974,0.0002245301,0.00004338438,0.00005568928,0.0001241032,0.000291434,0.00002459795,0.0001582148,4.360968e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492429,"about_ca_system_score_gemma":0.00002908356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001912624,"about_ca_topic_score_gemma":0.003211608,"domain_scores_codex":[0.9992929,0.00001595379,0.0001270986,0.0002401936,0.0001472943,0.0001765913],"domain_scores_gemma":[0.9992836,0.00004101183,0.0002102383,0.0002280256,0.0001947493,0.00004238202],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00112711,0.00008639384,0.05243732,0.02915259,0.0006480643,0.00001218602,0.2074392,0.169229,0.02404189,0.00840085,0.0001186846,0.5073067],"study_design_scores_gemma":[0.002938833,0.0003085192,0.3628632,0.005394508,0.0002635566,0.000001996423,0.09111384,0.5055945,0.02813017,0.001759705,0.0004776192,0.001153601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855569,0.0001021704,0.01316186,0.00004636099,0.0001468272,0.0008155396,0.00003127958,0.00003684552,0.0001021943],"genre_scores_gemma":[0.9937603,0.0002160451,0.004977161,0.000001627805,0.00001725994,0.00001362483,0.0001458105,0.00002128249,0.0008468807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5061531,"threshold_uncertainty_score":0.9156071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006287320502830724,"score_gpt":0.2036319369606179,"score_spread":0.1973446164577872,"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."}}