{"id":"W2069012785","doi":"10.1002/ajim.20845","title":"Are immigrants, ethnic and linguistic minorities over‐represented in jobs with a high level of compensated risk? Results from a montréal, Canada study using census and workers' compensation data","year":2010,"lang":"en","type":"article","venue":"American Journal of Industrial Medicine","topic":"Pesticide Exposure and Toxicity","field":"Agricultural and Biological Sciences","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Public Safety Canada; Institut de recherche Robert-Sauvé en santé et en sécurité du travail; Université du Québec à Montréal","funders":"","keywords":"Immigration; Ethnic group; Census; Medicine; Context (archaeology); Demography; Population; Ethnic composition; Demographic economics; Compensation (psychology); Gerontology; Environmental health; Psychology; Geography; Social psychology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006121469,0.0001363039,0.0005737749,0.0000392277,0.00006869326,0.00001196015,0.0001991501,0.00005124625,0.00000864072],"category_scores_gemma":[0.002505776,0.00005534061,0.0000108627,0.0004018572,0.0003443696,0.00006045007,0.00006477517,0.0005334855,1.43792e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000234134,"about_ca_system_score_gemma":0.00007470501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9729846,"about_ca_topic_score_gemma":0.9511501,"domain_scores_codex":[0.9984168,0.0002459892,0.000606954,0.0002122281,0.000368241,0.0001497739],"domain_scores_gemma":[0.9969629,0.001322198,0.001297382,0.0001066795,0.0001734987,0.0001373472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001913557,0.00009630578,0.9644039,9.789545e-7,0.00008640361,0.0001570471,0.0004205196,0.00001383622,0.007070387,4.983197e-7,0.0002571792,0.02557938],"study_design_scores_gemma":[0.003278936,0.001287102,0.9817092,0.0003446972,0.0001278204,0.00003117365,0.01282818,0.0001995702,0.00005825956,0.000009429687,0.00002949624,0.00009617591],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980614,0.000213032,0.00000148624,0.0006499193,0.0001990577,0.0001603481,0.0007092941,0.000002786211,0.000002672914],"genre_scores_gemma":[0.9991955,0.00003433244,0.0001348051,0.00003779595,0.0005519147,3.415837e-7,0.00004256267,0.000001307586,0.000001465688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02548321,"threshold_uncertainty_score":0.2999829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1411406026916057,"score_gpt":0.2932917869441624,"score_spread":0.1521511842525567,"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."}}