{"id":"W4389076848","doi":"10.32920/24653781.v1","title":"Showcasing Relationships between Neighborhood Design and Wellbeing Toronto Indicators","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Landscape ecology; Geography; Sustainability; Land use; Environmental resource management; Urbanization; Green infrastructure; Land cover; Sustainable development; Landscape planning; Index (typography); Cohesion (chemistry); Ecology; Regional science; Environmental planning; Computer science; Environmental science","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":[0.0004597488,0.0002537939,0.0001969297,0.001490103,0.0005359143,0.0008800476,0.0002868951,0.0001357848,0.00337664],"category_scores_gemma":[0.003218904,0.000128976,0.0002446131,0.00302374,0.0003676917,0.0003641232,0.0009954049,0.0002440531,0.0003170194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003094554,"about_ca_system_score_gemma":0.001509651,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5491619,"about_ca_topic_score_gemma":0.8250847,"domain_scores_codex":[0.9996151,0.00006257029,0.00002724166,0.00007080723,0.000159959,0.0000642831],"domain_scores_gemma":[0.9981943,0.0004007995,0.0003821201,0.0001755889,0.0005917066,0.000255536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005110922,0.000006601582,0.9863728,0.00003757426,0.00004523667,0.00004010354,0.001282645,0.0007476686,0.0002468624,0.0006506626,0.002663017,0.007855687],"study_design_scores_gemma":[9.013204e-7,0.00001024941,0.9954215,0.00001047041,0.00001150968,0.00001304363,0.00123244,0.0009126109,0.00012443,0.00007766742,0.002179944,0.000005250773],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816825,0.0001959735,0.0009387759,0.000155198,0.000009512772,0.0000197693,0.01031788,0.00005001574,0.006630342],"genre_scores_gemma":[0.9933845,0.00008809219,0.0006154664,0.000005866865,0.000003160086,0.0000222077,0.00492394,0.000009202136,0.0009475022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4508381,"threshold_uncertainty_score":0.9069865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08354137047349516,"score_gpt":0.2890310049912233,"score_spread":0.2054896345177281,"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."}}