{"id":"W7036789769","doi":"","title":"Dealing with relatives: Population-scale pedigrees in human genetics","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Cinema and Media Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Fonds de Recherche du Québec - Santé; Alfred P. Sloan Foundation","keywords":"Pedigree chart; Human genetic variation; Sampling bias; Bayesian probability; Genetic diversity; Genetic data; Population genetics; Diversity (politics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006715355,0.0004087571,0.000706621,0.002147746,0.001126292,0.002340127,0.0009456786,0.001093881,0.002850689],"category_scores_gemma":[0.03754386,0.0007402954,0.0006518293,0.003579791,0.002562742,0.004938435,0.001613609,0.002254709,0.0003358167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379181,"about_ca_system_score_gemma":0.001489697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01416614,"about_ca_topic_score_gemma":0.01809188,"domain_scores_codex":[0.9972692,0.002081624,0.00006826079,0.0002909763,0.0002283679,0.00006160413],"domain_scores_gemma":[0.9776444,0.0196343,0.0009045099,0.001004271,0.0004464364,0.0003661108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000400653,0.00005027865,0.02660889,0.0002145776,0.0001498325,0.0004338511,0.002582811,0.08925052,0.0002659388,0.6371228,0.008995373,0.2342852],"study_design_scores_gemma":[0.0000161571,0.00001394334,0.004982653,0.0001421013,0.00004320578,0.0003005615,0.0003312466,0.07965893,0.0000774924,0.9001673,0.01423683,0.00002960046],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04086316,0.009404936,0.9341283,0.01020817,0.00015775,0.00004400138,0.0003357108,0.0002501967,0.004607724],"genre_scores_gemma":[0.527836,0.01593952,0.4479182,0.001586518,0.0007428393,0.0001549548,0.00058499,0.0001587499,0.005078274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01416614,"threshold_uncertainty_score":0.03551465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514845914968492,"score_gpt":0.2400761403969185,"score_spread":0.2149276812472336,"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."}}