{"id":"W4384822396","doi":"10.3390/engproc2023036051","title":"A Framework for Smart Pavements in Canada","year":2023,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"","keywords":"Reliability (semiconductor); Durability; Computer science; Compromise; Pavement engineering; Task (project management); Empirical research; Pavement management; Data collection; Transport engineering; Engineering; Risk analysis (engineering); Systems engineering; Business; 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":[0.00002868523,0.00004590983,0.00005285287,0.00003167837,0.00001178754,0.00000500089,0.00004387384,0.00001984849,0.00001980042],"category_scores_gemma":[0.00001416163,0.00004248972,0.000009986681,0.0001236107,0.000001374477,0.00001927703,0.000008000497,0.00005219169,0.000006779321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001603422,"about_ca_system_score_gemma":0.0000419306,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.220219,"about_ca_topic_score_gemma":0.6853858,"domain_scores_codex":[0.9996303,9.806541e-7,0.00006973283,0.00004976841,0.00004715922,0.0002020931],"domain_scores_gemma":[0.9998795,0.0000304255,0.000003225693,0.00006080907,0.000006150945,0.00001987753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002374124,0.000008490159,0.3756218,0.0006013235,0.0001496339,0.0001351066,0.001358801,0.2325974,0.004124701,0.04653613,0.2353381,0.1035048],"study_design_scores_gemma":[0.001668851,0.00004794594,0.4297753,0.0004362561,0.00001518516,0.000003031255,0.004187401,0.2020293,0.0465158,0.08399127,0.2301034,0.001226313],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580194,0.00002122644,0.03238113,0.0001266592,0.003732487,0.0002599006,0.00001142994,0.0002961637,0.005151617],"genre_scores_gemma":[0.9961967,0.000006533786,0.003415812,0.00007399838,0.00008489368,0.00003760065,0.000003613335,0.00001199908,0.0001688123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4651668,"threshold_uncertainty_score":0.7849737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092854579185617,"score_gpt":0.2242864571959553,"score_spread":0.2133579114040991,"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."}}