{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1321886,0.001628043,0.001288765,0.007643172,0.001120204,0.005420235,0.0019935,0.001059456,0.003734002],"category_scores_gemma":[0.2587152,0.0004028426,0.001694428,0.006300237,0.002018556,0.0049655,0.00339036,0.00171119,0.000971341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003516599,"about_ca_system_score_gemma":0.004747419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001877549,"about_ca_topic_score_gemma":0.002802977,"domain_scores_codex":[0.8669833,0.08297221,0.01045472,0.004681792,0.03350011,0.001407787],"domain_scores_gemma":[0.6621279,0.2442917,0.02265305,0.01081631,0.05803812,0.002072954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002178105,0.001183892,0.0825224,0.006830478,0.001097153,0.0001709249,0.009805131,0.006454135,0.006705552,0.02067748,0.00787378,0.854501],"study_design_scores_gemma":[0.001875403,0.03251743,0.4680396,0.01350659,0.004014647,0.001065949,0.05896541,0.1402147,0.04498955,0.0529918,0.1800626,0.001756334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6299401,0.01364634,0.2158298,0.00296976,0.002139209,0.01674831,0.002370239,0.0008398294,0.1155164],"genre_scores_gemma":[0.6700076,0.00331477,0.3164769,0.0004379351,0.0002537113,0.005452044,0.000890274,0.0001082451,0.003058474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1321886,"threshold_uncertainty_score":0.6990883,"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."}}