{"id":"W7083330475","doi":"10.1016/j.ress.2025.111742","title":"Urban road network resilience assessment framework: Integrating spatiotemporal analysis with the resilience triangle and temporal performance indicators","year":2025,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"City of Calgary; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates; Indian Institute of Science","keywords":"Resilience (materials science); Metric (unit); Network analysis; Performance metric; Psychological resilience; Downtown; Network performance","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.00280107,0.0002922794,0.000503673,0.0001595644,0.0004790417,0.0001785542,0.0009531616,0.0001564641,0.000002955423],"category_scores_gemma":[0.0003248804,0.0001961031,0.0001099515,0.003273058,0.000128156,0.0002814382,0.0002785219,0.0005691651,0.000001036246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398483,"about_ca_system_score_gemma":0.0002089552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001286881,"about_ca_topic_score_gemma":0.00001546023,"domain_scores_codex":[0.9975851,0.0001860157,0.0005982822,0.0007559996,0.0003914239,0.0004831695],"domain_scores_gemma":[0.9977125,0.0005467882,0.0002506301,0.001254837,0.0001277427,0.0001075384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003208032,0.00002517911,0.4892152,0.0004918033,0.000144106,0.000005685823,0.0004662068,0.4837846,0.00002102608,0.02185035,0.0001370959,0.003826662],"study_design_scores_gemma":[0.0001360133,0.00006054215,0.3169301,0.0004393982,0.00006429177,0.000005212813,0.0002032248,0.67992,0.00006764443,0.00003867947,0.00192434,0.0002105429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1591379,0.0002068247,0.8360084,0.001638735,0.0001981964,0.0004120077,0.000002377938,0.0002894924,0.002106024],"genre_scores_gemma":[0.9623624,0.000009145555,0.03726542,0.00003622072,0.00005813426,0.00005628392,0.000004453187,0.000004153649,0.0002037834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8032245,"threshold_uncertainty_score":0.7996853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002955810756244757,"score_gpt":0.2047779470804236,"score_spread":0.2018221363241788,"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."}}