{"id":"W4361279137","doi":"10.12924/cis2023.11010001","title":"Evaluating the Effectiveness of Commercially Developed Appraisal Instruments (CDAIs) Using Composite Indices to Assess, Compare, and Rank the Liveability, Quality of Living and Sustainability Performance of Cities and Communities","year":2023,"lang":"en","type":"article","venue":"Challenges in Sustainability","topic":"Sustainable Building Design and Assessment","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Weighting; Ranking (information retrieval); Rank (graph theory); Sustainability; Identification (biology); Relevance (law); Composite index; Computer science; Selection (genetic algorithm); Management science; Composite indicator; Statistics; Data mining; Mathematics; Econometrics; Machine learning; Engineering; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01481595,0.0002866211,0.0007725455,0.0002181292,0.000279654,0.00004426661,0.000378039,0.00009347428,0.000001547619],"category_scores_gemma":[0.002954805,0.0002217575,0.00004953989,0.0005429542,0.001293302,0.0002606611,0.000888691,0.0003381762,2.06233e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003731247,"about_ca_system_score_gemma":0.0003093502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001269392,"about_ca_topic_score_gemma":0.0002865167,"domain_scores_codex":[0.9952081,0.002856701,0.0008258661,0.000296433,0.0003982148,0.0004146945],"domain_scores_gemma":[0.9841458,0.0142129,0.0002114793,0.0005933498,0.0007741539,0.0000623257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003263224,0.000104556,0.9141589,0.02370232,0.00007920155,5.601278e-7,0.02941932,0.01608841,0.0003610523,0.002087432,3.659589e-7,0.01367155],"study_design_scores_gemma":[0.0003403314,0.0001984908,0.8234619,0.000552555,0.00002772699,0.000001838132,0.1508556,0.02130304,0.0003621151,0.002716234,0.000002709313,0.0001774494],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964808,0.001521664,0.000175243,0.0001304306,0.0000711156,0.001492995,0.00001216186,0.00005270674,0.00006290049],"genre_scores_gemma":[0.9988945,0.0007165437,0.0002418192,0.000003892144,0.000009742059,0.0001082941,0.000002252736,0.00002199817,9.892198e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1214363,"threshold_uncertainty_score":0.904301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1672440280513468,"score_gpt":0.414764954925024,"score_spread":0.2475209268736772,"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."}}