{"id":"W4205221638","doi":"10.1002/9780470015902.a0005186","title":"Genetic Discrimination","year":2009,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Context (archaeology); Genetic testing; Normative; Actuarial science; Genetic discrimination; Variety (cybernetics); Business; Health care; Public economics; Economics; Political science; Medicine; Economic growth; Law; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004877866,0.000590647,0.0005547709,0.001061863,0.002650131,0.003340655,0.001437773,0.004651643,0.06766309],"category_scores_gemma":[0.01673617,0.0002046052,0.0005821396,0.0008697697,0.004680845,0.002104969,0.003714904,0.003735433,0.01630051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936921,"about_ca_system_score_gemma":0.002947937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003440511,"about_ca_topic_score_gemma":0.003615635,"domain_scores_codex":[0.9939293,0.002059149,0.0002900865,0.001092453,0.001660502,0.0009686033],"domain_scores_gemma":[0.9926676,0.002601292,0.0007391445,0.00143647,0.001725276,0.0008301465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008729973,0.00008827035,0.008826804,0.0001449307,0.00002299124,0.001522161,0.002251462,0.000198053,0.0007163027,0.7033234,0.1464325,0.1363859],"study_design_scores_gemma":[0.00003640159,0.00008630576,0.005696719,0.0004950115,0.00002838362,0.004156988,0.001398076,0.0002984012,0.0007695197,0.1862983,0.8006919,0.00004397986],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02194979,0.005283047,0.01328805,0.0695729,0.005698777,0.0002118119,0.001167757,0.0002348303,0.882593],"genre_scores_gemma":[0.4559937,0.006968501,0.009387803,0.08749258,0.003478369,0.0004110155,0.00166588,0.000210419,0.4343918],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.06766309,"threshold_uncertainty_score":0.2263556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04438201316881967,"score_gpt":0.2787496078895468,"score_spread":0.2343675947207271,"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."}}