{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003707167,0.0001281326,0.0003267413,0.0005185629,0.00004817,0.00001442631,0.0003653091,0.0001069655,0.001256445],"category_scores_gemma":[0.0001529136,0.0001312177,0.00006165962,0.0002824463,0.0001495619,0.00005849785,0.00004084919,0.00006619525,0.0004096788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001916456,"about_ca_system_score_gemma":0.00006793925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077681,"about_ca_topic_score_gemma":0.0004689133,"domain_scores_codex":[0.9988853,0.00001473984,0.0004524945,0.0003469991,0.00007395546,0.0002264534],"domain_scores_gemma":[0.9991774,0.00001978028,0.0005066762,0.0002061647,0.00000697045,0.00008301326],"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":[8.757944e-7,0.00005196018,0.003431779,0.0001594639,0.00001490065,0.000001027758,0.0002830665,0.00001000805,7.007796e-8,0.2972658,0.6851994,0.01358171],"study_design_scores_gemma":[0.00006627796,0.0000643846,0.009044645,0.00004273446,0.000003126396,1.607377e-7,0.00003409721,0.00003477706,2.538269e-7,0.01598669,0.9745717,0.000151157],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0001103787,0.008178266,0.0005269696,0.002155154,0.0006519948,0.0001951854,0.00006424139,0.00004085784,0.9880769],"genre_scores_gemma":[0.01630187,0.0274587,0.007976095,0.001318398,0.001219247,0.00002789442,0.00001085903,0.0001108438,0.9455761],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2893723,"threshold_uncertainty_score":0.9996566,"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."}}