{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03761854,0.0003479996,0.0004159026,0.001453469,0.003472517,0.0224075,0.002939205,0.003919101,0.007647184],"category_scores_gemma":[0.07879099,0.0007358522,0.0006011265,0.003740474,0.003651525,0.0134005,0.007750104,0.009695295,0.001894003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006413018,"about_ca_system_score_gemma":0.01906112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02430338,"about_ca_topic_score_gemma":0.02214859,"domain_scores_codex":[0.9748727,0.008981414,0.001693683,0.001213043,0.009387828,0.003851284],"domain_scores_gemma":[0.863367,0.09471653,0.006766118,0.004930163,0.02248645,0.007733762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001031059,0.0004897608,0.02029687,0.001639053,0.00006464313,0.001680061,0.03801074,0.006542259,0.002805416,0.5491217,0.08680946,0.2924369],"study_design_scores_gemma":[0.00005562149,0.0002883579,0.01987213,0.006107287,0.0001058886,0.00169183,0.1172713,0.01786818,0.00466645,0.1495726,0.6823407,0.0001595778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2763643,0.008638159,0.07179764,0.3573211,0.0009595033,0.0006202671,0.0004422838,0.000676567,0.2831802],"genre_scores_gemma":[0.930358,0.00531696,0.03564375,0.006667064,0.000217574,0.0001844135,0.0003038108,0.0003697492,0.02093867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03761854,"threshold_uncertainty_score":0.1989482,"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."}}