{"id":"W4385983712","doi":"10.1186/s43065-023-00085-6","title":"Interdependence of social-ecological-technological systems in Phoenix, Arizona: consequences of an extreme precipitation event","year":2023,"lang":"en","type":"article","venue":"Journal of Infrastructure Preservation and Resilience","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Institute of Standards and Technology; National Science Foundation","keywords":"Causal loop diagram; Computer science; Anthropocene; Climate change; Ecological systems theory; Earth system science; Resilience (materials science); Event (particle physics); Disturbance (geology); Complex adaptive system; Risk analysis (engineering); Adaptation (eye); Environmental resource management; System dynamics; Ecology; Environmental science; Business; Psychology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009086651,0.0001329494,0.0004154457,0.0004461397,0.00005108239,0.00002499452,0.0003437427,0.0002050934,0.00003604317],"category_scores_gemma":[0.000540608,0.00009752103,0.00007358693,0.0009001603,0.0004229006,0.000592055,0.00005152729,0.0003372408,3.349189e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005271074,"about_ca_system_score_gemma":0.00005661955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001832393,"about_ca_topic_score_gemma":0.00002290583,"domain_scores_codex":[0.9981757,0.0001596475,0.0009257178,0.0001493688,0.0004130407,0.0001764594],"domain_scores_gemma":[0.9989107,0.0001904434,0.0004362259,0.0001306857,0.0002794342,0.00005249181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00007311171,0.00003489137,0.02024884,0.000259503,0.00003698548,0.00001280085,0.001337495,0.7675387,0.1952953,0.002751864,0.00008108107,0.01232948],"study_design_scores_gemma":[0.0005066313,0.0003671118,0.7956026,0.0002042885,0.00003556182,0.00005994692,0.004645631,0.1596614,0.02568528,0.01285397,0.0001787333,0.0001988021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936253,0.0002383477,0.005593686,0.0001259357,0.0001337721,0.000135443,0.000007409945,0.00002814771,0.0001119364],"genre_scores_gemma":[0.9987186,0.0002779218,0.0009432069,0.000007435897,0.00002837095,0.000005004054,0.00000339761,0.000005329484,0.00001078296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7753538,"threshold_uncertainty_score":0.3976792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595677587630663,"score_gpt":0.2644912682727464,"score_spread":0.2485344923964397,"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."}}