{"id":"W7162004841","doi":"10.82308/43852","title":"Making space: lessons from Canadian COVID-19 street reallocations","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public transport; Corporate governance; Preparedness; Equity (law); Placemaking; Traffic congestion; Destinations; Closing (real estate)","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":[0.005314871,0.0008661209,0.0005031559,0.001484696,0.0539537,0.01135259,0.003485577,0.002823981,0.007851802],"category_scores_gemma":[0.006299482,0.0005273127,0.0005391265,0.003098954,0.01988193,0.004492887,0.008334682,0.00657318,0.0007353268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1345004,"about_ca_system_score_gemma":0.1841718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9808971,"about_ca_topic_score_gemma":0.99501,"domain_scores_codex":[0.9939752,0.001767732,0.00009617486,0.0004513912,0.001424153,0.002285275],"domain_scores_gemma":[0.9935461,0.001409847,0.0001858925,0.0002470692,0.001936801,0.002674247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006008212,0.00008677781,0.007649003,0.0002354851,0.00001497836,0.002366224,0.8542595,0.000518214,0.0006736639,0.0310264,0.06482596,0.03828376],"study_design_scores_gemma":[0.000006885353,0.00002123333,0.003344221,0.0002049914,0.000008024814,0.0001192263,0.7646762,0.0001461756,0.0002050577,0.001928999,0.2292964,0.00004264379],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5841675,0.00485442,0.003216672,0.1245449,0.00104452,0.0002911461,0.0008371065,0.0001564796,0.2808872],"genre_scores_gemma":[0.9483638,0.003900385,0.002899923,0.00757721,0.00006795648,0.00009077059,0.0003280173,0.0001363327,0.03663561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1345004,"threshold_uncertainty_score":0.9758729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07475056742641126,"score_gpt":0.413442649266391,"score_spread":0.3386920818399798,"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."}}