{"id":"W1486273257","doi":"10.1109/icsmc.2002.1176362","title":"Sustainable development: Decision making using fuzzy logic and sensitivity analysis","year":2003,"lang":"en","type":"article","venue":"","topic":"Sustainable Development and Environmental Policy","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sustainability; Fuzzy logic; Sustainable development; Sensitivity (control systems); Process (computing); Social sustainability; Computer science; Risk analysis (engineering); Environmental economics; Business; Economics; Engineering; Artificial intelligence; Political science; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007279405,0.0002044872,0.0002125329,0.0001762786,0.0004738563,0.00007799004,0.00005852999,0.00007627634,0.001975048],"category_scores_gemma":[0.00009756873,0.0001849277,0.00005327068,0.001007823,0.0001360986,0.0003752904,0.0003269389,0.00008030307,0.0001205994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006228678,"about_ca_system_score_gemma":0.00002347961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003359886,"about_ca_topic_score_gemma":0.0001359277,"domain_scores_codex":[0.9983425,0.0000791813,0.0002331893,0.0004463276,0.0002999876,0.0005988053],"domain_scores_gemma":[0.999535,0.00007743687,0.00006400578,0.0001929732,0.000006301274,0.0001242553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003493495,0.0001417062,0.9427157,0.00002610456,0.0002064267,0.0004884169,0.001654117,0.02608012,0.001255964,0.005794459,0.0002858112,0.02131624],"study_design_scores_gemma":[0.0005526987,0.00002412478,0.9576723,0.0000119588,0.0002117241,0.0001007395,0.007754742,0.005271139,0.00204765,0.01338097,0.01204326,0.0009287014],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9373377,0.00003073362,0.01906582,0.00001742064,0.00001819777,0.0001523701,2.55378e-7,0.00003658922,0.0433409],"genre_scores_gemma":[0.939199,0.000008205203,0.05711136,0.0003030893,0.000005687219,0.000004373803,0.000002190189,0.00001274852,0.003353403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03998749,"threshold_uncertainty_score":0.9989373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01347890691319545,"score_gpt":0.2458685732720736,"score_spread":0.2323896663588781,"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."}}