{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001383341,0.0001642507,0.000164149,0.00008577848,0.00008885109,0.00004766511,0.0001040826,0.00009188276,0.00001927401],"category_scores_gemma":[0.00004297205,0.0001498348,0.00001964748,0.0002151096,0.00001625254,0.0002796929,0.000009923984,0.0001235898,0.000007300021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000101602,"about_ca_system_score_gemma":0.00001357376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001547246,"about_ca_topic_score_gemma":0.000263517,"domain_scores_codex":[0.9991046,0.00002105287,0.0001843122,0.0003429501,0.0001329567,0.0002140882],"domain_scores_gemma":[0.9995156,0.000060201,0.00002508009,0.000292553,0.00004388365,0.00006267534],"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":[0.0007787295,0.0001726883,0.0135387,0.00041735,0.0005749586,0.00006367236,0.03109124,0.1149749,0.1169625,0.005744419,0.1096448,0.6060361],"study_design_scores_gemma":[0.005239628,0.0003980843,0.6329988,0.0009493851,0.0002854182,0.00006137217,0.01840435,0.2059343,0.09301375,0.0160723,0.02418797,0.002454685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4410568,0.00004021762,0.5555786,0.000005469256,0.0005688668,0.0003389052,0.0000518925,0.000227355,0.002131823],"genre_scores_gemma":[0.9361373,0.00001020637,0.0634384,0.00003679802,0.0001121778,0.00004516125,0.0001535757,0.00002586597,0.00004052948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.61946,"threshold_uncertainty_score":0.6110085,"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."}}