{"id":"W796242764","doi":"","title":"FROM ROAD CONDITION DATA COLLECTION TO EFFECTIVE MAINTENANCE DECISION MAKING: SASKATCHEWAN HIGHWAYS AND TRANSPORTATION APPROACH","year":2003,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data collection; Transport engineering; Pavement management; Asset management; Profiling (computer programming); Data collection system; Computer science; Operations research; Engineering; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001807368,0.0009588329,0.0006362321,0.003570572,0.001928261,0.005526365,0.001985656,0.0009799514,0.003595263],"category_scores_gemma":[0.004554656,0.0008916428,0.0005509074,0.005437209,0.001416529,0.002078759,0.002345058,0.001942764,0.0007648359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02420917,"about_ca_system_score_gemma":0.03829993,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7825164,"about_ca_topic_score_gemma":0.9010519,"domain_scores_codex":[0.9978846,0.0007503601,0.0001571888,0.0003363775,0.0006373074,0.0002342598],"domain_scores_gemma":[0.9963177,0.001089293,0.0001872097,0.0003161951,0.001865607,0.0002239646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003055308,0.0005774542,0.08591519,0.0007234436,0.0007262549,0.002500675,0.003244043,0.12758,0.008786156,0.08033554,0.0306151,0.6586907],"study_design_scores_gemma":[0.000287977,0.000455993,0.2063692,0.001449449,0.0007869158,0.001132295,0.02833256,0.3986616,0.01169596,0.1815674,0.1685851,0.0006756875],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2559672,0.006610387,0.4899996,0.03327651,0.0005078745,0.002871985,0.006623118,0.001508429,0.202635],"genre_scores_gemma":[0.7299242,0.006093343,0.2298512,0.002598214,0.000108421,0.0009560761,0.003202178,0.0001258401,0.02714054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2174836,"threshold_uncertainty_score":0.4375288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008014380406589647,"score_gpt":0.2341452134235436,"score_spread":0.226130833016954,"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."}}