{"id":"W4403371077","doi":"10.19184/ejlh.v11i2.43512","title":"Analysing Discrimination based on Genetic Information","year":2024,"lang":"en","type":"article","venue":"Lentera Hukum","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006203813,0.00006145293,0.00004129603,0.0001594831,0.00009522479,0.0002907195,0.0001967844,0.00001932128,0.00001258198],"category_scores_gemma":[0.000004995487,0.00005481928,0.00004550045,0.0003368575,0.00001028726,0.0007659348,0.000030571,0.00006341891,0.0001630041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004713867,"about_ca_system_score_gemma":0.0000227918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005126654,"about_ca_topic_score_gemma":0.000001416662,"domain_scores_codex":[0.9994737,0.00001595722,0.0001324635,0.0001398942,0.0001423369,0.00009566269],"domain_scores_gemma":[0.9996606,0.00003017766,0.00002246329,0.0002272705,0.00003031688,0.00002923342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003243529,0.0001395206,0.0004857204,0.00009853912,0.00002358523,0.000009916936,0.001499493,0.0304571,0.0004479777,0.3654366,0.004386961,0.5970113],"study_design_scores_gemma":[0.0000554936,0.00002600853,0.009639321,0.00003795138,0.000004817419,0.000002582845,0.000008572,0.9633983,0.0001105519,0.002537533,0.02411639,0.00006243039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00403672,0.00005031622,0.9890001,0.003835111,0.0002501443,0.00007688216,0.00000282419,0.000175234,0.002572684],"genre_scores_gemma":[0.9636595,0.000004626471,0.03544337,0.0005935393,0.00006666852,0.00002819971,0.00003663796,0.000003102155,0.000164312],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9596228,"threshold_uncertainty_score":0.2803415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009128101008679345,"score_gpt":0.2432785179633171,"score_spread":0.2341504169546378,"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."}}