{"id":"W4409493187","doi":"10.1139/cjce-2024-0309","title":"Integration of GIS and GPS as a decision support tool in a GAMS-based network-level pavement maintenance optimisation system","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pavement management; Transport engineering; Geographic information system; Global Positioning System; Government (linguistics); Decision support system; Computer science; Information system; Civil engineering; Engineering; Geography; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0009090035,0.0008057745,0.0008226106,0.001304936,0.0003493672,0.001192282,0.001163522,0.0005026433,0.004559601],"category_scores_gemma":[0.001762272,0.0004754059,0.0005021443,0.0009299382,0.0003188763,0.0009793441,0.001037991,0.000457532,0.0009478322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007420191,"about_ca_system_score_gemma":0.0007851525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01339711,"about_ca_topic_score_gemma":0.01215846,"domain_scores_codex":[0.9996285,0.00009794649,0.00003797346,0.0000949876,0.0001029912,0.00003748335],"domain_scores_gemma":[0.9994296,0.0002090118,0.00005979145,0.00006925454,0.0001835233,0.00004881447],"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":[0.0003718773,0.0002744549,0.008385836,0.0001458289,0.000107492,0.0002533857,0.0002592082,0.8464906,0.005439658,0.003516804,0.003428329,0.1313265],"study_design_scores_gemma":[0.00001987841,0.00002952844,0.0006569875,0.000007001537,0.00001670887,0.00001557359,0.00003927074,0.9958289,0.001011343,0.0009471214,0.001417042,0.00001066444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09708671,0.00009107905,0.8642598,0.0003216312,0.00005189113,0.0003620983,0.001611568,0.02700805,0.009207045],"genre_scores_gemma":[0.6164854,0.00009375344,0.3791798,0.00006710686,0.00001706799,0.0002719627,0.001219839,0.0002539332,0.002411113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01339711,"threshold_uncertainty_score":0.02663821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00630606857758978,"score_gpt":0.1953832861880529,"score_spread":0.1890772176104631,"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."}}