{"id":"W4389430711","doi":"10.7202/1108003ar","title":"BILL S-231: The Ethics of Familial and Genetic Genealogical Searching in Criminal Investigations","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Bioethics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Windsor; Université du Québec à Montréal","funders":"Genome Canada","keywords":"Legislation; Criminal justice; Economic Justice; Internet privacy; Criminal investigation; Genetic discrimination; Genetic engineering; Political science; Business; Criminology; Law; Psychology; Genetic testing; Computer science; Genetics; Biology; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01063785,0.00009501788,0.0002812808,0.0004456037,0.000215168,0.00003770929,0.0003175972,0.0006289528,0.00002382193],"category_scores_gemma":[0.05598995,0.0000590873,0.00009190353,0.0007421181,0.00340346,0.00003947469,0.00006662333,0.008035974,0.000006487267],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001135288,"about_ca_system_score_gemma":0.01096868,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01735355,"about_ca_topic_score_gemma":0.1452885,"domain_scores_codex":[0.9972811,0.0005624631,0.0007023195,0.0001550142,0.0009227738,0.0003763816],"domain_scores_gemma":[0.9848942,0.01273029,0.0001706877,0.0002738839,0.000989138,0.0009417955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000346193,0.0001421366,0.7096916,0.002621431,0.0003138595,0.006872389,0.08211603,0.001547434,0.01026744,0.1616656,0.00281767,0.02159814],"study_design_scores_gemma":[0.001461544,0.001419013,0.7430436,0.00161514,0.0001488063,0.0007237878,0.01012134,0.002641944,0.001164248,0.2351164,0.002323605,0.000220564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8433207,0.0004741938,0.00009510556,0.1555246,0.0001815086,0.0001457619,0.0000190632,0.000004150209,0.0002349553],"genre_scores_gemma":[0.9920053,0.002248661,0.002984526,0.002403555,0.0002140488,0.000001801526,0.000002674651,0.00001586212,0.0001235938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.153121,"threshold_uncertainty_score":0.9993087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7097133608807258,"score_gpt":0.5628185966970455,"score_spread":0.1468947641836803,"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."}}