{"id":"W4296880162","doi":"10.54394/gtrm8209","title":"COVID-19 among migrant farmworkers in Canada","year":2022,"lang":"en","type":"book","venue":"ILO eBooks","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Canada","funders":"International Labour Organization; Mitacs; Canada Research Chairs; University of Windsor","keywords":"Migrant workers; Economic shortage; Coronavirus disease 2019 (COVID-19); Pandemic; Agriculture; Political science; Economic growth; Farm workers; 2019-20 coronavirus outbreak; Socioeconomics; Geography; Business; Sociology; Medicine; Economics; Outbreak","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005226451,0.0002057569,0.0001945466,0.001095831,0.003784827,0.001862045,0.0006397893,0.0005605448,0.003820858],"category_scores_gemma":[0.002061422,0.0001757389,0.0001933934,0.002536397,0.001118761,0.000564651,0.001234721,0.001094304,0.0002565675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04514017,"about_ca_system_score_gemma":0.05703749,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959325,"about_ca_topic_score_gemma":0.998176,"domain_scores_codex":[0.9992625,0.00003013636,0.00001049145,0.00004341862,0.0001779232,0.0004754569],"domain_scores_gemma":[0.9987831,0.00008949412,0.0001996769,0.00002087518,0.000402607,0.0005043244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000190312,0.000122855,0.8427781,0.0002095728,0.00004305141,0.001246947,0.01529533,0.001456952,0.0004512053,0.00459632,0.07119288,0.06241645],"study_design_scores_gemma":[0.00001169133,0.00003333167,0.9286737,0.0002851721,0.00001485312,0.0001033677,0.04284189,0.0005448953,0.0001026682,0.0004355352,0.02692237,0.00003057097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527253,0.003918306,0.0001185129,0.009141505,0.000104644,0.00006218532,0.006681642,0.00002740244,0.02722059],"genre_scores_gemma":[0.9835798,0.003899831,0.0001468606,0.001014376,0.00002993073,0.00003031137,0.002140273,0.000009692836,0.009148878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04514017,"threshold_uncertainty_score":0.3275163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04183611385468043,"score_gpt":0.2307053317002646,"score_spread":0.1888692178455842,"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."}}