{"id":"W7117965830","doi":"10.1007/978-3-032-01074-2_22","title":"Barriers to Implementing Data Analytics Solutions in Asset Management: The Case of Data Quality in Road Infrastructure","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pavement management; Data quality; Mistake; Asset management; International Roughness Index; Asset (computer security); Quality (philosophy); Index (typography); Analytics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001101669,0.0005701742,0.0006939413,0.0009884816,0.00006586222,0.00005875879,0.001662169,0.0003775056,0.00006079217],"category_scores_gemma":[0.0005600965,0.0005455707,0.000066478,0.0005840446,0.00003414766,0.0002503525,0.001856472,0.001493255,9.283639e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004233306,"about_ca_system_score_gemma":0.00008395362,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004275423,"about_ca_topic_score_gemma":0.02905631,"domain_scores_codex":[0.9972035,0.00002648475,0.001028694,0.0007040204,0.0002420407,0.0007952374],"domain_scores_gemma":[0.9969766,0.0003053025,0.0001114529,0.002468933,0.00003825347,0.0000994212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007142755,0.00000267624,0.0006225526,0.0008957646,0.0001696989,0.000425315,0.0003578281,0.9572091,0.00007737062,0.002939916,0.0004573818,0.03683528],"study_design_scores_gemma":[0.001172098,0.00002171138,0.004017909,0.004253915,0.0002758373,0.0001241169,0.000266161,0.934925,0.0001385514,0.005088659,0.04789753,0.001818575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008586263,0.007072569,0.8929152,0.0006852679,0.005857805,0.004685777,0.008639859,0.0007341296,0.07082307],"genre_scores_gemma":[0.9893613,0.0002698962,0.009083422,0.00007092881,0.000292881,0.00002552914,0.0006177896,0.0001245982,0.0001536522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9807751,"threshold_uncertainty_score":0.9996996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289242528194293,"score_gpt":0.2883875350378424,"score_spread":0.2594632822184131,"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."}}