{"id":"W3136175746","doi":"","title":"SUBURBAN IMMIGRANT SETTLEMENTS IN TORONTO AND TRANSPORTATION IMPLICATIONS","year":2021,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Human settlement; Geography; Transport engineering; Regional science; Engineering; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003226684,0.0002256905,0.0001839336,0.000702812,0.002954586,0.001556291,0.0005016761,0.000283227,0.006370991],"category_scores_gemma":[0.001541051,0.0001096249,0.0003069499,0.002411533,0.001172662,0.000559839,0.001755357,0.0004169915,0.0001877727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01111669,"about_ca_system_score_gemma":0.009224825,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8779712,"about_ca_topic_score_gemma":0.9306424,"domain_scores_codex":[0.9996729,0.00008512121,0.00001399511,0.00003450627,0.00005806777,0.00013557],"domain_scores_gemma":[0.9993382,0.00007585355,0.0001492495,0.00002238812,0.0001640333,0.0002502579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001669361,0.00009657477,0.8275256,0.0004487028,0.00007920467,0.003679922,0.08138476,0.002248495,0.0005990549,0.02482017,0.01807196,0.04087863],"study_design_scores_gemma":[0.000007988257,0.00004545657,0.8299605,0.000303103,0.00005063054,0.0003063887,0.150233,0.000885329,0.00009888806,0.001043775,0.01704017,0.00002465343],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802066,0.001503376,0.0001200997,0.003036813,0.00006287563,0.0000245017,0.0008595269,0.00001134386,0.01417479],"genre_scores_gemma":[0.9968701,0.0009884879,0.00006243354,0.00008078328,0.00001126918,0.000009605634,0.0002111819,0.000002486687,0.001763684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1220288,"threshold_uncertainty_score":0.2454949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109775365474384,"score_gpt":0.2508576212537679,"score_spread":0.2398800847063295,"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."}}