{"id":"W3090857295","doi":"10.1007/978-3-030-48675-4_23","title":"Legal Aspects of Genetic Testing Regarding Insurance and Employment","year":2020,"lang":"en","type":"book-chapter","venue":"Ius comparatum","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Genetic testing; Genetic discrimination; Political science; Relevance (law); Social security; Social insurance; Corporate governance; Law and economics; Business; Law; Sociology; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009409294,0.0003423207,0.0006113382,0.00009094337,0.0001154226,0.0001687035,0.0007792423,0.0001273235,0.00001768698],"category_scores_gemma":[0.00006649374,0.000298507,0.00009977305,0.0000972681,0.0001908141,0.0001927305,0.0005082019,0.0003826761,0.00006094714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004561697,"about_ca_system_score_gemma":0.0001485658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006532585,"about_ca_topic_score_gemma":0.00001980648,"domain_scores_codex":[0.9981527,0.00002720658,0.0005051164,0.0006705731,0.0003826068,0.0002617778],"domain_scores_gemma":[0.9987357,0.0001351945,0.0002646915,0.0005449787,0.0001659864,0.0001534939],"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":[0.00002910146,0.00003611635,0.000180518,0.0002109095,0.0001398884,0.0001975968,0.0006237575,0.0001248949,0.001265053,0.9561878,0.0215942,0.01941018],"study_design_scores_gemma":[0.00100133,0.00152583,0.001453965,0.001544882,0.00007525537,0.0003823344,0.000008826322,0.07885086,0.003787832,0.06559645,0.8440097,0.001762748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0007945427,0.00358141,0.009915888,0.0001517328,0.0006817057,0.0002815858,0.000008767105,0.0001615892,0.9844228],"genre_scores_gemma":[0.9338033,0.0001085632,0.0136373,0.0004172104,0.0002609629,0.000006271632,0.000002971894,0.00004956498,0.05171384],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9330088,"threshold_uncertainty_score":0.9999467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0562724255300803,"score_gpt":0.2390547971079101,"score_spread":0.1827823715778298,"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."}}