{"id":"W4410632623","doi":"10.22215/etd/2025-16493","title":"Aiming for Adaptation: Developing a Quantitative Framework for Building Resilience in Response to Climate Change-Induced Grid Outages","year":2025,"lang":"en","type":"dissertation","venue":"","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Resilience (materials science); Climate change; Adaptation (eye); Climate change adaptation; Grid; Environmental resource management; Computer science; Environmental science; Environmental planning; Geography; Psychology; Ecology; Biology; Materials science","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":[0.005232727,0.001431176,0.0004711957,0.002181897,0.000565578,0.002945328,0.001255846,0.0009345204,0.003481743],"category_scores_gemma":[0.008764938,0.0003079914,0.001064029,0.001286878,0.0024779,0.003699744,0.002471747,0.00177612,0.0003608564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002815506,"about_ca_system_score_gemma":0.002290439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004791116,"about_ca_topic_score_gemma":0.00320572,"domain_scores_codex":[0.9976301,0.001317212,0.0001121062,0.00026167,0.0004619391,0.0002169424],"domain_scores_gemma":[0.9972504,0.001517119,0.0003938653,0.0002621847,0.0004335647,0.0001428558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004151406,0.0001272644,0.004111735,0.0004088415,0.000109138,0.0001336575,0.001080072,0.3984815,0.002884508,0.5083404,0.002414392,0.08186699],"study_design_scores_gemma":[0.00001116658,0.0001888664,0.004527184,0.0005618214,0.00006242519,0.0001042968,0.00185284,0.5405874,0.002677412,0.4262144,0.02310861,0.0001036718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03396784,0.0008758385,0.9229181,0.002259097,0.0001065155,0.0003441092,0.0004057744,0.0004430319,0.03867976],"genre_scores_gemma":[0.6728317,0.001269058,0.3204748,0.0002604334,0.00005858995,0.0006120277,0.0003388238,0.0001671798,0.003987445],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005232727,"threshold_uncertainty_score":0.0276736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05009465434448863,"score_gpt":0.3610527181111386,"score_spread":0.31095806376665,"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."}}