{"id":"W4318218903","doi":"10.1007/s44223-022-00020-x","title":"The evaluation of urban spatial quality and utility trade-offs for Post-COVID working preferences: a case study of Hong Kong","year":2023,"lang":"en","type":"article","venue":"Architectural Intelligence","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBI Group (Canada); Arcadis (Canada)","funders":"University of Queensland","keywords":"Amenity; Neighbourhood (mathematics); Work (physics); Vitality; Residence; Perception; Geography; Urban design; Preference; Psychology; Business; Architecture; Demographic economics; Engineering; Economics","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":[0.005524507,0.0001197073,0.0002240659,0.00006227772,0.0005900543,0.00004383429,0.0003277692,0.00005474821,0.00001700294],"category_scores_gemma":[0.001242513,0.00008345523,0.00008291362,0.0004696019,0.0006144952,0.0000856529,0.00004743591,0.0001369987,2.413053e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003134043,"about_ca_system_score_gemma":0.0001946059,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04612225,"about_ca_topic_score_gemma":0.2013186,"domain_scores_codex":[0.9973796,0.0007601255,0.0005901451,0.0003358206,0.0006556511,0.0002786715],"domain_scores_gemma":[0.9974183,0.001739814,0.0002623468,0.0002699689,0.0002307403,0.00007877572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001359473,0.00008769608,0.4785486,0.00005225768,0.00003110083,0.000003038538,0.09425808,0.0000825403,0.00008199629,0.0001321893,0.000001262332,0.4265853],"study_design_scores_gemma":[0.000281669,0.0003367137,0.8758541,0.00004669242,0.0001150952,0.000002454541,0.1017912,0.006785497,0.001155366,0.01341927,0.00002739766,0.0001845205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971567,0.0001648543,0.0005179686,0.0002574965,0.0001665217,0.00150532,0.00001851529,0.00004294991,0.0001697199],"genre_scores_gemma":[0.9997895,0.00001031081,0.00004145079,0.000008928601,0.00006290917,0.00006263728,0.000005140407,0.000005410165,0.00001365308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4264007,"threshold_uncertainty_score":0.9602297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.252835940302934,"score_gpt":0.4379062302234804,"score_spread":0.1850702899205464,"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."}}