{"id":"W2789264594","doi":"10.22054/qjpl.2017.15453.1365","title":"Genetic Discrimination in the Canada and Iran's Legal System","year":2018,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Race, Genetics, and Society","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002103857,0.0001829733,0.0002553331,0.001799861,0.0178283,0.004693901,0.0009818227,0.002819037,0.00360362],"category_scores_gemma":[0.004821981,0.0001613995,0.0003195166,0.002721623,0.009810711,0.0009146825,0.002085797,0.002855806,0.0001804059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07367849,"about_ca_system_score_gemma":0.1445467,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.971387,"about_ca_topic_score_gemma":0.9815638,"domain_scores_codex":[0.9962363,0.0003855569,0.00007856832,0.0002139092,0.00137925,0.001706438],"domain_scores_gemma":[0.997568,0.0004573165,0.0002867919,0.0000618941,0.0009570601,0.0006689645],"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":[0.00003429643,0.00005458605,0.02319617,0.00005551263,0.00001976762,0.002009859,0.02424477,0.0004141449,0.0003478399,0.864404,0.04799285,0.03722612],"study_design_scores_gemma":[0.000060504,0.00005000817,0.06128824,0.0005544251,0.00008511268,0.001962678,0.05730077,0.001468891,0.0007275857,0.0621502,0.8141339,0.000217639],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4139009,0.01101887,0.001284121,0.1892321,0.001246773,0.0001249069,0.000341483,0.00006048594,0.3827904],"genre_scores_gemma":[0.9517291,0.003479259,0.001328236,0.01983078,0.0001904777,0.00001938754,0.00005513708,0.00001863673,0.02334897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07367849,"threshold_uncertainty_score":0.5345774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0930329444596983,"score_gpt":0.4543947934122902,"score_spread":0.3613618489525919,"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."}}