{"id":"W4213429078","doi":"10.1101/2022.02.21.481346","title":"SexFindR: A computational workflow to identify young and old sex chromosomes","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and Clinical Aspects of Sex Determination and Chromosomal Abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Fisheries and Oceans Canada; U.S. Geological Survey; Great Lakes Fishery Commission; New York State Department of Environmental Conservation","keywords":"Biology; Evolutionary biology; Genomics; Comparative genomics; Genome; Population genomics; Genetics; Computational biology; Gene","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":[0.003324909,0.002576558,0.001591437,0.003328652,0.001615365,0.003773895,0.003375919,0.002004399,0.01777494],"category_scores_gemma":[0.009020944,0.001276925,0.0031457,0.00208363,0.0008552163,0.001874874,0.003682956,0.002182343,0.01125727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097313,"about_ca_system_score_gemma":0.003184672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005792742,"about_ca_topic_score_gemma":0.00942855,"domain_scores_codex":[0.9984427,0.0001625362,0.0001838041,0.0007261729,0.0003508518,0.0001339694],"domain_scores_gemma":[0.9981345,0.0009172193,0.0001569676,0.0003289726,0.0003017231,0.0001606172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003814636,0.0005279219,0.04974492,0.004632104,0.00233748,0.002752208,0.001785474,0.02846865,0.06247007,0.01958141,0.5113132,0.312572],"study_design_scores_gemma":[0.001658864,0.000388583,0.02983974,0.0007629953,0.0005808306,0.002018973,0.0009609161,0.3733519,0.06722346,0.08778223,0.4347189,0.0007126156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.02471939,0.002096104,0.3617151,0.0009491303,0.0006810437,0.0006414862,0.1316991,0.4722091,0.005289575],"genre_scores_gemma":[0.09086789,0.001436636,0.5740367,0.002024433,0.0002361994,0.002359353,0.2831402,0.03867946,0.007219218],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01777494,"threshold_uncertainty_score":0.05946308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189748190905489,"score_gpt":0.2584363111239077,"score_spread":0.2465388292148528,"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."}}