{"id":"W2164929725","doi":"10.3141/1968-06","title":"Performance Evaluation of Sensor- and Image-Based Technologies for Automated Pavement Condition Surveys","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Transportation of Ontario; University of Waterloo","funders":"","keywords":"Vendor; Software; Data collection; Computer science; Christian ministry; Set (abstract data type); Variance (accounting); Transport engineering; Data science; Engineering management; Data mining; Engineering; Marketing; Business; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00861732,0.0007340792,0.0006575637,0.002291332,0.0005609738,0.001365198,0.001468887,0.001313026,0.001680716],"category_scores_gemma":[0.02194761,0.000411726,0.000409154,0.001735768,0.000407636,0.001812482,0.0008084525,0.0003148,0.0006540701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214886,"about_ca_system_score_gemma":0.0006388528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006975416,"about_ca_topic_score_gemma":0.0072086,"domain_scores_codex":[0.9924893,0.002646636,0.0003998869,0.001073952,0.003011182,0.0003789687],"domain_scores_gemma":[0.9763291,0.01157499,0.001261561,0.00157648,0.008677304,0.0005806508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01347705,0.002714856,0.214684,0.001381021,0.0005029398,0.0004216796,0.001855769,0.06675868,0.1504362,0.001665007,0.003833947,0.5422688],"study_design_scores_gemma":[0.0004843213,0.01949493,0.2650609,0.0001274344,0.0005758985,0.0006598573,0.002184045,0.5520982,0.1509757,0.0007129008,0.007312964,0.0003128299],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612464,0.000505285,0.03453701,0.000122935,0.00008035205,0.0002292512,0.0003650169,0.0007954566,0.002118272],"genre_scores_gemma":[0.9623619,0.000189833,0.03581943,0.00005130882,0.00002846635,0.00009880193,0.0004101975,0.00005008519,0.0009900383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00861732,"threshold_uncertainty_score":0.04557329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04857896631516306,"score_gpt":0.3562795384197109,"score_spread":0.3077005721045479,"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."}}