{"id":"W6944986295","doi":"10.25318/9810008801-eng","title":"The impact of the COVID-19 pandemic on income by percentage change in income between 2019 and 2020, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts","year":2022,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Metropolitan area; Government (linguistics); Pandemic; Household income; American Community Survey; County government","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":[0.00101529,0.001183984,0.001133457,0.003174375,0.001008064,0.002386679,0.002577218,0.001217101,0.01911156],"category_scores_gemma":[0.00660104,0.0005930032,0.001329434,0.009429865,0.0004138566,0.001058579,0.001463036,0.002452466,0.006630728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01169402,"about_ca_system_score_gemma":0.02522852,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9690982,"about_ca_topic_score_gemma":0.9742674,"domain_scores_codex":[0.9989753,0.0000713052,0.0001084876,0.0001432703,0.000359385,0.000342267],"domain_scores_gemma":[0.9962599,0.0002649343,0.0004108566,0.0001568413,0.00243591,0.0004716136],"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":[0.00008966202,0.00002134762,0.02098327,0.0004937473,0.00009923497,0.0000309096,0.00005338093,0.0008006215,0.00003009469,0.0008675403,0.9737021,0.002828051],"study_design_scores_gemma":[0.0005934255,0.00004454551,0.3589825,0.001847463,0.0002506939,0.0002122178,0.001066204,0.005049218,0.0004624512,0.001142283,0.630187,0.000161905],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008830203,0.0001366553,0.00003032728,0.0002405712,0.0000304485,0.00001036238,0.9979692,0.00004021813,0.0006591416],"genre_scores_gemma":[0.00875019,0.0003684689,0.0002562544,0.0001976431,0.00002109386,0.00008626265,0.9885226,0.00004066157,0.001756752],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03090179,"threshold_uncertainty_score":0.0848465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964470543100366,"score_gpt":0.3106341140958743,"score_spread":0.2909894086648707,"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."}}