{"id":"W3213216091","doi":"10.32920/ryerson.14662044.v1","title":"Planning for the grey tsunami housing shock in the city of Toronto","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; McGill University","funders":"","keywords":"Baby boom; Boom; Stock (firearms); Baby boomers; Population; Demographic economics; Business; Population ageing; Economic growth; Geography; Economics; Demography; Engineering; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003205292,0.0003213979,0.0002061469,0.0003984249,0.005991736,0.003547253,0.0007305364,0.001221517,0.01343785],"category_scores_gemma":[0.001055957,0.0002576284,0.0003514455,0.0009065307,0.0009799033,0.0009531425,0.002014687,0.001289019,0.0009300772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03608635,"about_ca_system_score_gemma":0.05041546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9304672,"about_ca_topic_score_gemma":0.9849503,"domain_scores_codex":[0.999665,0.00004891503,0.000004657329,0.00001879787,0.00005840421,0.0002042683],"domain_scores_gemma":[0.9992543,0.0000401659,0.00002947146,0.00001626411,0.0001810928,0.0004787958],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003904296,0.0002255867,0.08106441,0.0008070879,0.0001383337,0.006482893,0.03640458,0.02611064,0.003827413,0.1011482,0.6489945,0.09440592],"study_design_scores_gemma":[0.00007358339,0.0002760083,0.2089365,0.0006480475,0.0001435157,0.0003530853,0.2595516,0.01486995,0.001365259,0.01414203,0.4994601,0.0001803458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6560422,0.005426656,0.005074207,0.09243475,0.001533661,0.0007060842,0.007539172,0.0002402867,0.2310032],"genre_scores_gemma":[0.9350743,0.003153464,0.002850613,0.001673413,0.0001417004,0.0001344111,0.002156849,0.00005669743,0.05475853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06953275,"threshold_uncertainty_score":0.261826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06572713679066174,"score_gpt":0.3590805017948571,"score_spread":0.2933533650041954,"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."}}