{"id":"W4411492823","doi":"10.3390/app15136973","title":"Resilience Investment Against Extreme Weather Events Considering Critical Load Points in an Active Microgrid","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Resilience (materials science); Extreme weather; Reliability engineering; Microgrid; Computer science; Criticality; Vulnerability (computing); Critical infrastructure; Risk analysis (engineering); Investment (military); Business; Environmental resource management; Engineering; Environmental science; Computer security; Climate change","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":[0.0009352633,0.000634934,0.0004503759,0.0007744617,0.0003729654,0.0008458783,0.0005794092,0.000477955,0.001251658],"category_scores_gemma":[0.002586869,0.0001868944,0.0002878447,0.0004200846,0.0006421176,0.001260789,0.0008873238,0.0003804664,0.00005662393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001428421,"about_ca_system_score_gemma":0.0009646023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007060947,"about_ca_topic_score_gemma":0.004915828,"domain_scores_codex":[0.999711,0.0001130065,0.00001018586,0.00003725986,0.00005592221,0.00007271796],"domain_scores_gemma":[0.9992316,0.0003975284,0.0001519459,0.00004500119,0.0001006716,0.0000731935],"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":[0.00006774011,0.00001971489,0.001285968,0.00001371952,0.00001318666,0.00007465055,0.00001767248,0.989463,0.0008274209,0.003438534,0.0001014041,0.004676946],"study_design_scores_gemma":[0.000005467502,0.00007050439,0.0008276249,0.00000426428,0.00001666716,0.00001917204,0.00005786401,0.9959124,0.0005717723,0.002387802,0.0001210803,0.000005389752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7852464,0.0002132028,0.206526,0.0003374347,0.00003024422,0.00007634756,0.0001177927,0.0002667093,0.007185821],"genre_scores_gemma":[0.997516,0.0000296474,0.002142146,0.000004479368,0.000002514884,0.000008340419,0.00001172955,0.0000044356,0.0002807523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007060947,"threshold_uncertainty_score":0.0140397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114281590009446,"score_gpt":0.2754720823881536,"score_spread":0.2543292664880591,"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."}}