{"id":"W4237783641","doi":"10.32920/ryerson.14661639","title":"The place for immigrants in Toronto's transit and transportation city","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Public transport; Immigration; Demographics; Limiting; Transit (satellite); Work (physics); Public policy; Economic Justice; Sociology; Geography; Regional science; Business; Public relations; Political science; Transport engineering; Economic growth; Engineering; Economics","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":[0.0009541084,0.0003829904,0.000324937,0.000567004,0.01802919,0.004255749,0.0007797977,0.001141797,0.005413615],"category_scores_gemma":[0.001668175,0.0002268895,0.0002985795,0.001440057,0.01070489,0.001654973,0.004529973,0.001415765,0.0001984686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02107476,"about_ca_system_score_gemma":0.01740163,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7417767,"about_ca_topic_score_gemma":0.885513,"domain_scores_codex":[0.9986995,0.0005916841,0.000027172,0.00007081393,0.000119847,0.000490849],"domain_scores_gemma":[0.9988043,0.0002402333,0.0001421511,0.00003376989,0.0001408393,0.0006386773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00003285024,0.000005533316,0.006870151,0.0000634883,0.000005708887,0.001288598,0.9818503,0.00006782998,0.0004484679,0.004526409,0.002194633,0.002645987],"study_design_scores_gemma":[0.000001344831,0.00001026947,0.003350453,0.0000462169,0.00000497688,0.00008927505,0.9837431,0.00002128295,0.00003433861,0.0001206862,0.01256998,0.000008016737],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739127,0.001219643,0.0003786906,0.004708128,0.0001276738,0.00002615597,0.0001228401,0.00001622646,0.01948787],"genre_scores_gemma":[0.9949946,0.0004599478,0.0001230582,0.0003288482,0.00001288252,0.00001327106,0.00003080195,0.000009008278,0.004027501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2582233,"threshold_uncertainty_score":0.519488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533389994971528,"score_gpt":0.3225678402310148,"score_spread":0.2972339402812995,"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."}}