{"id":"W4410017078","doi":"10.1016/j.habitatint.2025.103426","title":"Assessing the impact of urban amenities on people with disabilities in London: A multiscale geographically weighted regression analysis","year":2025,"lang":"en","type":"article","venue":"Habitat International","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Geographically Weighted Regression; Regression analysis; Geography; Regression; Regional science; Econometrics; Statistics; Demographic economics; Economic geography; Economics; Mathematics","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":[0.0004079148,0.0001076316,0.00021243,0.0003329793,0.0001897492,0.0001423436,0.0003440053,0.00005475867,0.0003004432],"category_scores_gemma":[0.000132318,0.00006065711,0.0002228867,0.001112085,0.00052721,0.000356937,0.00002317953,0.000149813,8.879847e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001517979,"about_ca_system_score_gemma":0.0001777804,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01048105,"about_ca_topic_score_gemma":0.03965597,"domain_scores_codex":[0.9987486,0.00015481,0.0002757827,0.0002118032,0.000442095,0.0001668986],"domain_scores_gemma":[0.998868,0.0006411809,0.0001108499,0.0001711843,0.000176589,0.00003212244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009007334,0.0002023845,0.9909903,0.000006394382,0.0002158737,0.00000113316,0.00479768,0.0001274378,0.00001879215,0.002935874,0.0001043715,0.000509646],"study_design_scores_gemma":[0.0002679122,0.00003363723,0.9910164,0.00009951313,0.00005541479,4.63853e-8,0.005445577,0.0008816917,0.00003795883,0.001999915,0.00009124049,0.00007062321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852766,0.00006286701,0.0003249581,0.001304234,0.00009317876,0.0001510894,0.00002672406,0.00002252877,0.01273776],"genre_scores_gemma":[0.9990524,0.00001100437,0.0001466771,0.00003800583,0.00003963549,0.00002408109,0.00003513846,0.000003716352,0.0006493204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02917492,"threshold_uncertainty_score":0.9961082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572732648939153,"score_gpt":0.3535040705345079,"score_spread":0.3377767440451164,"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."}}