{"id":"W4235160750","doi":"10.1002/9780470015902.a0005186.pub2","title":"Genetic Discrimination","year":2014,"lang":"en","type":"other","venue":"Encyclopedia of Life Sciences","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Context (archaeology); Normative; Genetic testing; Actuarial science; Variety (cybernetics); Business; Genetic discrimination; Order (exchange); Public economics; Risk analysis (engineering); Economics; Political science; Medicine; Law; Computer science","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":[0.0008301659,0.0001810302,0.0003170918,0.000490029,0.0002789208,0.0000409639,0.0008357128,0.0002197315,0.00344231],"category_scores_gemma":[0.0003287444,0.0001484958,0.0001038309,0.0006750226,0.0017655,0.00008465529,0.00005726895,0.0001015993,0.00009395282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008590018,"about_ca_system_score_gemma":0.0004943283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004872906,"about_ca_topic_score_gemma":0.01714488,"domain_scores_codex":[0.9977373,0.0002614574,0.0002972704,0.0004157441,0.0009390484,0.0003491169],"domain_scores_gemma":[0.9990464,0.0001322673,0.0004068713,0.0002018203,0.0000382636,0.0001743726],"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":[0.000001472422,0.00005019533,0.02448739,0.00007049202,0.00002705383,7.247029e-7,0.004678217,0.000002895958,5.688203e-7,0.0247391,0.8915556,0.05438629],"study_design_scores_gemma":[0.00006956052,0.00005130303,0.01093281,0.0000803009,0.00002481913,1.156545e-7,0.001649685,0.000004911817,2.948194e-7,0.003399299,0.9835733,0.0002135975],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004152812,0.003209041,0.0002145885,0.0005629752,0.00139655,0.0001650301,0.0000168452,0.0001260082,0.9938937],"genre_scores_gemma":[0.0613975,0.01985501,0.004501704,0.000148044,0.002145748,0.00002723124,0.000007421325,0.00009924154,0.9118181],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.0920177,"threshold_uncertainty_score":0.9974687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289804121284884,"score_gpt":0.3022095006823748,"score_spread":0.2732290885538864,"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."}}