{"id":"W1768123436","doi":"","title":"Performance Assessment Methodology for Sustainable Pavement Marking","year":2009,"lang":"en","type":"article","venue":"Transportation Research Board 88th Annual MeetingTransportation Research Board","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Truck; Transport engineering; Artificial neural network; Range (aeronautics); Pavement management; Engineering; Regression analysis; Computer science; Machine learning","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":[0.003079582,0.001306601,0.0006567786,0.002184571,0.0003732374,0.001349241,0.001140929,0.000782392,0.003265392],"category_scores_gemma":[0.008966537,0.0002801522,0.0008077341,0.001393838,0.0004290581,0.001660749,0.0009738731,0.0009238392,0.0006873246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234452,"about_ca_system_score_gemma":0.001505461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003467005,"about_ca_topic_score_gemma":0.001819813,"domain_scores_codex":[0.9979644,0.0006502401,0.0001559215,0.0002853839,0.0008277108,0.0001162351],"domain_scores_gemma":[0.9972582,0.001218584,0.0003962068,0.0001427184,0.000933564,0.00005062979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004855757,0.000144298,0.003743618,0.0002820109,0.00007571225,0.0000923256,0.0001866496,0.7579131,0.004220775,0.03031332,0.001524056,0.2014556],"study_design_scores_gemma":[0.000005659062,0.0001093511,0.001114431,0.00004175794,0.00001450944,0.00003593599,0.00008639287,0.9793144,0.001592774,0.01435556,0.003309497,0.00001963682],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006310334,0.0001219608,0.9897286,0.00005369203,0.00001551802,0.0001364599,0.0001033776,0.000342373,0.003187784],"genre_scores_gemma":[0.4926951,0.0004547606,0.5010864,0.00005514463,0.00004397636,0.0009074081,0.000747476,0.0001227973,0.003886987],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003467005,"threshold_uncertainty_score":0.01628655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0637700126259883,"score_gpt":0.3943697894977068,"score_spread":0.3305997768717185,"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."}}