{"id":"W4414894778","doi":"10.1061/jitse4.iseng-2697","title":"Enhancing Road Asset Management with CityGML Enriched by Public Inputs: A Comprehensive Approach to Pothole Repair Prioritization","year":2025,"lang":"en","type":"article","venue":"Journal of Infrastructure Systems","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pothole (geology); Asset (computer security); Asset management; Decision support system; Sociotechnical system; Building information modeling; Artificial neural network","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":[0.0002338484,0.0002235285,0.0004007057,0.0003967926,0.00009173094,0.0001485679,0.0002916648,0.0001177633,0.000002584257],"category_scores_gemma":[0.00002780262,0.0001892051,0.00007357941,0.0007647676,0.00001942529,0.0002088201,0.00005051057,0.0003187231,0.000002506533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003264092,"about_ca_system_score_gemma":0.00006299919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000143654,"about_ca_topic_score_gemma":0.000004239668,"domain_scores_codex":[0.9983754,0.00005455154,0.0007408364,0.0001940394,0.0003874523,0.0002477237],"domain_scores_gemma":[0.9987912,0.00002924011,0.0002342403,0.0003201045,0.0004975222,0.0001277571],"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":[0.00002319078,0.00005065776,0.00118279,0.0009921428,0.0006280681,0.000006323611,0.0005424391,0.9434323,0.01658597,0.002300022,0.03245266,0.001803475],"study_design_scores_gemma":[0.006570359,0.0006882091,0.05519858,0.003795716,0.0009201946,0.00100379,0.007189875,0.7390104,0.005653272,0.001838389,0.1754805,0.002650715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06690276,0.0005028475,0.9281617,0.0001167213,0.000522899,0.0006320374,0.00001661266,0.0001748839,0.002969566],"genre_scores_gemma":[0.9290116,0.00002530984,0.07046422,0.0001242731,0.0001528509,0.00005904111,0.00002407677,0.00003286298,0.0001057979],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8621088,"threshold_uncertainty_score":0.7715561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005100105958492613,"score_gpt":0.2111437960935638,"score_spread":0.2060436901350712,"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."}}