{"id":"W4286207865","doi":"10.1101/2022.07.20.500680","title":"On the Genes, Genealogies, and Geographies of Quebec","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; McGill University; McGill Genome Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Genetic genealogy; Genealogy; Population; Geography; Range (aeronautics); Metadata; Evolutionary biology; History; Demography; Biology; Sociology; Computer science; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000566726,0.0003195296,0.0002941455,0.0001352261,0.0001834345,0.00006186209,0.0005966841,0.0002961518,0.00007251306],"category_scores_gemma":[0.0001811825,0.0002583751,0.0001379989,0.0002184536,0.0004986487,0.000001981098,0.001320648,0.0004183442,0.000003282843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002831367,"about_ca_system_score_gemma":0.0004318096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007087637,"about_ca_topic_score_gemma":0.00007835117,"domain_scores_codex":[0.9981015,0.000195691,0.000298275,0.0006932379,0.0003621003,0.0003491501],"domain_scores_gemma":[0.9981077,0.00005642615,0.0001997761,0.001336045,0.0001968747,0.0001031941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001086955,0.00009552583,0.00777252,0.0002216854,0.0003371059,0.00001284789,0.000007724444,0.000180742,0.9818571,0.003872967,0.005499003,0.00003410862],"study_design_scores_gemma":[0.0004653108,0.0004423983,0.1259845,0.0000858964,0.00008218264,3.653952e-8,0.00002722362,0.00006856137,0.8072126,0.00003433265,0.06486866,0.0007282875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988116,0.01038344,0.000113235,0.0003212683,0.0003105311,0.0004708272,0.0002298276,0.00002451899,0.00003030581],"genre_scores_gemma":[0.993978,0.004768007,0.0005754073,0.0002171209,0.0001709831,0.000175887,0.000001974337,0.00005880287,0.00005380304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1746445,"threshold_uncertainty_score":0.9999868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0131735160981683,"score_gpt":0.2297450606640032,"score_spread":0.2165715445658349,"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."}}