{"id":"W4396617856","doi":"10.17803/lexgen-2022-1-1-56-62","title":"Genetic Information in the Light of Genetic Discrimination: the Experience of Foreign States","year":2022,"lang":"en","type":"article","venue":"Lex genetica.","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genetic discrimination; Genetics; Psychology; Biology; Genetic testing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005284411,0.0001764454,0.0003749497,0.0009302429,0.00595803,0.004668591,0.0004153706,0.00227076,0.003970235],"category_scores_gemma":[0.009173262,0.0001166347,0.0002143628,0.001854521,0.00893643,0.004114114,0.003440263,0.002650605,0.0002538762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002769073,"about_ca_system_score_gemma":0.002739066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044005,"about_ca_topic_score_gemma":0.008837522,"domain_scores_codex":[0.9964361,0.002666653,0.00008657961,0.0001596606,0.00023736,0.0004136241],"domain_scores_gemma":[0.9951544,0.003312995,0.0005185957,0.0001937476,0.0002566043,0.0005637293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001054555,0.00006789884,0.0225301,0.0001878081,0.00002453984,0.005401721,0.8200991,0.0001913312,0.000391055,0.08841968,0.01675273,0.04582858],"study_design_scores_gemma":[0.000009481947,0.0001251122,0.01255062,0.0006501877,0.00002557575,0.003460173,0.7375396,0.0001618344,0.0003446591,0.01486916,0.2302181,0.00004544638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8069955,0.01435179,0.001017627,0.08766162,0.0007332082,0.00001437251,0.0001111419,0.00001625503,0.08909848],"genre_scores_gemma":[0.9838668,0.004758587,0.0001762382,0.005766097,0.00009494818,0.000004306016,0.00003062196,0.000007561836,0.005294757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01044005,"threshold_uncertainty_score":0.02794701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02016596324085793,"score_gpt":0.2099452000861683,"score_spread":0.1897792368453104,"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."}}