{"id":"W6941861454","doi":"10.1371/journal.pone.0212706.g001","title":"Canada’s pre- and post-arrival immigrant surveillance system for tuberculosis.","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Immigration; Population; Ethnic group; Government (linguistics); Refugee","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":[0.0009754759,0.0004583968,0.0003110023,0.00146448,0.002373572,0.001271444,0.001276933,0.0008688262,0.03743043],"category_scores_gemma":[0.003833687,0.0002972111,0.0003952401,0.001790895,0.0003932845,0.0004877005,0.001161886,0.001129634,0.005288875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02278004,"about_ca_system_score_gemma":0.1109277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9945398,"about_ca_topic_score_gemma":0.9970202,"domain_scores_codex":[0.9992613,0.00007602558,0.00003255823,0.0000650275,0.0003087449,0.000256388],"domain_scores_gemma":[0.9948724,0.0002144582,0.0001378564,0.0001097729,0.00301742,0.001648141],"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":[0.0002177641,0.00006993252,0.03063521,0.0002376945,0.0000356446,0.0001394256,0.000283146,0.0001819691,0.0004109319,0.002436789,0.8838226,0.08152894],"study_design_scores_gemma":[0.0001172173,0.00007664224,0.2521801,0.0009267439,0.00005595705,0.0001900265,0.001614833,0.0009612747,0.0005184259,0.0008040901,0.7424781,0.00007644419],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0336937,0.009618893,0.002881856,0.03358151,0.001867502,0.0008341651,0.5866565,0.001826239,0.3290396],"genre_scores_gemma":[0.2695922,0.01285104,0.01653209,0.01900958,0.0003819105,0.0008430035,0.206281,0.0006614293,0.4738478],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03743043,"threshold_uncertainty_score":0.1652815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008769525184116512,"score_gpt":0.1906062319210065,"score_spread":0.18183670673689,"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."}}