{"id":"W4242641901","doi":"10.32920/ryerson.14637051","title":"Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Geological Survey","keywords":"Geographic information system; Real estate; Geography; Urban morphology; Environmental quality; Principal component analysis; Population; Census; Geographically Weighted Regression; Regression analysis; Cartography; Urban planning; Computer science; Environmental resource management; Environmental planning; Environmental science; Statistics; Civil engineering; Engineering; Business; Mathematics; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0037759,0.0009020203,0.0007534745,0.003147029,0.0003012102,0.001224559,0.0008034762,0.0003679379,0.001400238],"category_scores_gemma":[0.01729143,0.0003352271,0.0007179309,0.003610804,0.000250043,0.001167745,0.0009208227,0.000571258,0.001037257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003131423,"about_ca_system_score_gemma":0.0005340719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02155859,"about_ca_topic_score_gemma":0.02079283,"domain_scores_codex":[0.9978162,0.0009142152,0.0001745583,0.0004705229,0.0005270363,0.00009739258],"domain_scores_gemma":[0.9952749,0.002172256,0.0004715775,0.0006997468,0.001349544,0.00003188737],"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":[0.0002002958,0.0001518981,0.09393384,0.0003269567,0.0005294847,0.0001752132,0.0007238133,0.2654134,0.02360205,0.004230807,0.001893253,0.6088189],"study_design_scores_gemma":[0.000014076,0.000041721,0.04446036,0.00004025973,0.00008971306,0.00007817752,0.0003271269,0.9408963,0.008441204,0.002169438,0.003386047,0.00005550731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1347908,0.0002131252,0.8587289,0.0000908522,0.00003308602,0.00007467228,0.0005496147,0.002673747,0.002845246],"genre_scores_gemma":[0.6775418,0.0002333852,0.319929,0.00002573486,0.00001554344,0.00005728546,0.0009412581,0.0003211865,0.0009348336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02155859,"threshold_uncertainty_score":0.04286617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317228813745861,"score_gpt":0.2914357517840456,"score_spread":0.2682634636465869,"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."}}