{"id":"W4409793567","doi":"10.61091/jcmcc127a-256","title":"A multi-stage dynamic planning-based optimization method for post-disaster resilience enhancement and flexible resource dispatch of distribution networks in coastal cities","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Southern Power Grid","keywords":"Resilience (materials science); Stage (stratigraphy); Resource distribution; Resource (disambiguation); Distribution (mathematics); Computer science; Environmental resource management; Operations research; Resource allocation; Environmental science; Engineering; Geology; Mathematics; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001154784,0.0001902625,0.0005673149,0.0001951489,0.0001228128,0.00008152161,0.0001565735,0.0001313427,0.000001668794],"category_scores_gemma":[0.0002899338,0.0001782993,0.00009763257,0.00032504,0.00007778235,0.0001217608,0.00006447519,0.0002608277,1.562046e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008928729,"about_ca_system_score_gemma":0.00006380132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000122946,"about_ca_topic_score_gemma":0.00000358327,"domain_scores_codex":[0.9984373,0.00008244633,0.0008775642,0.0001515818,0.0002257299,0.0002254067],"domain_scores_gemma":[0.9984783,0.00069434,0.0003673839,0.0001294447,0.0002741978,0.00005632409],"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":[0.000183269,0.0001473742,0.0005170222,0.0006870147,0.00006129189,0.000001639237,0.0005897704,0.9836448,0.0007315308,0.01219748,0.00001508359,0.001223706],"study_design_scores_gemma":[0.002562715,0.0002791332,0.0002859226,0.0006188584,0.00007598852,0.000002409219,0.0008633406,0.9864847,0.001231688,0.007411048,0.0000381825,0.0001460284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2822461,0.0002007855,0.7164229,0.00002350839,0.0008753935,0.0001927459,0.000009017788,0.0000114624,0.00001813378],"genre_scores_gemma":[0.9566652,0.00001546506,0.04321117,0.000008309034,0.00006559779,0.000004099637,0.00001365567,0.00001244168,0.000004091342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6744191,"threshold_uncertainty_score":0.7270836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008662359756855151,"score_gpt":0.2811737714771677,"score_spread":0.2725114117203126,"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."}}