{"id":"W3177783298","doi":"10.1007/978-3-030-43699-5_1","title":"Legal Aspects of Genetic Testing Regarding Insurance and Employment","year":2020,"lang":"en","type":"book-chapter","venue":"Ius comparatum","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":1,"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":[0.00008215941,0.0003316141,0.0005181781,0.0001491651,0.0001327794,0.0001672656,0.0002307026,0.0001240096,0.0001108464],"category_scores_gemma":[0.0000760404,0.000301259,0.00008759753,0.0000882471,0.00008862871,0.0002200236,0.0002548332,0.0003541792,0.0003038681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001915267,"about_ca_system_score_gemma":0.00003237418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002827532,"about_ca_topic_score_gemma":0.00004661168,"domain_scores_codex":[0.9986596,0.000004803969,0.0004408426,0.0003982801,0.000281564,0.0002149754],"domain_scores_gemma":[0.9991759,0.00004948748,0.0003632794,0.000203834,0.0001868209,0.00002065664],"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":[0.0004146487,0.00009493353,0.00546471,0.002791779,0.0004811426,0.0005154509,0.0001925151,0.000470578,0.001451642,0.917629,0.05250797,0.01798563],"study_design_scores_gemma":[0.0007426701,0.0001058548,0.003620483,0.001602106,0.0001788225,0.00003057107,0.00001451323,0.00721284,0.0001634788,0.02024499,0.965166,0.0009176777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01013998,0.001209919,0.00003354299,0.00008933264,0.0005385215,0.0002857534,0.000006894644,0.0000867593,0.9876093],"genre_scores_gemma":[0.9582933,0.00002043862,0.0002213314,0.0006587834,0.0008401833,0.000004554258,0.00001772539,0.0000620062,0.03988169],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9481533,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1387021633074395,"score_gpt":0.2297225802899001,"score_spread":0.09102041698246069,"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."}}