{"id":"W4385693503","doi":"10.32920/23913186","title":"The role of urban and rural greenspaces in shaping immigrant wellbeing and settlement in place","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Immigration; Settlement (finance); Geography; Diversity (politics); Neighbourhood (mathematics); Equity (law); Environmental justice; Sociology; Economic growth; Political science; Business","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":[0.001555628,0.0001824388,0.0001996229,0.0006365218,0.005693315,0.004549801,0.0005066111,0.0005395866,0.005458475],"category_scores_gemma":[0.002253755,0.0001273509,0.000306279,0.0007755843,0.005733934,0.001545056,0.005904692,0.0009991203,0.0002446061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002312955,"about_ca_system_score_gemma":0.003783002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05468506,"about_ca_topic_score_gemma":0.1716629,"domain_scores_codex":[0.9991443,0.000408948,0.0000217454,0.00007191982,0.0000922394,0.0002608232],"domain_scores_gemma":[0.9991595,0.0002198481,0.0001260322,0.00006177018,0.0001037118,0.0003291186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001070189,0.0001417181,0.2309235,0.0002849464,0.00007500174,0.002010077,0.6466684,0.0005952183,0.001737739,0.04204002,0.003358695,0.07205771],"study_design_scores_gemma":[0.000005923178,0.00006874051,0.1284383,0.0003060819,0.000029332,0.0002859164,0.831762,0.000263493,0.0001829427,0.006713341,0.03190076,0.00004308092],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655546,0.0005082547,0.0009464776,0.002051263,0.00005199608,0.00002383846,0.0000621236,0.000009591332,0.03079176],"genre_scores_gemma":[0.9980368,0.0002597813,0.0003242526,0.0001640461,0.000005643369,0.00001174848,0.00001723038,0.000005737189,0.001174924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05468506,"threshold_uncertainty_score":0.1087335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01671686786568177,"score_gpt":0.2462458519589067,"score_spread":0.229528984093225,"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."}}