{"id":"W3200988931","doi":"10.1007/s42650-021-00053-z","title":"Counting the Dead: COVID-19 and Mortality in Quebec and British Columbia During the First Wave","year":2021,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research","keywords":"Excess mortality; Coronavirus disease 2019 (COVID-19); Demography; Pandemic; Geography; Death toll; Mortality rate; Population; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Medicine; Disease; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006037702,0.00006392992,0.0001982942,0.00006596577,0.0004310258,0.0002057451,0.00005253602,0.00005234924,0.00002662468],"category_scores_gemma":[0.001731183,0.00008253033,0.00001626406,0.0002595961,0.00011079,0.0001288146,0.00006292843,0.0001211276,0.000001110481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312582,"about_ca_system_score_gemma":0.0001079668,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9877594,"about_ca_topic_score_gemma":0.9998808,"domain_scores_codex":[0.9991449,0.00002478016,0.0003219495,0.0002474167,0.00002656906,0.000234438],"domain_scores_gemma":[0.9994682,0.0001720743,0.0001014245,0.0001583473,0.00001745049,0.00008246741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[5.697906e-7,0.000002038237,0.9953233,0.00007008066,0.00001650042,0.00003942943,0.003147255,0.00009396581,9.330872e-8,0.0005964413,0.000367849,0.0003424934],"study_design_scores_gemma":[0.0002703983,0.00000175997,0.9853536,0.00003357094,0.000003513246,0.00001615549,0.001647849,0.0005122166,8.190328e-8,0.004983228,0.00708139,0.00009616176],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911577,0.00592846,0.000003334643,0.002193809,0.0001661316,0.0001905015,0.00005822714,0.000005784877,0.0002960125],"genre_scores_gemma":[0.9969386,0.000613628,0.000006861883,0.00203871,0.00004365774,0.00001998141,0.000008231418,0.000007827349,0.0003225204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01212143,"threshold_uncertainty_score":0.3432356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07092645657895909,"score_gpt":0.2812492539145671,"score_spread":0.210322797335608,"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."}}