{"id":"W2966443090","doi":"10.1186/s12864-019-5998-1","title":"Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants","year":2019,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université Laval","funders":"Grain Farmers of Ontario; Saskatchewan Pulse Growers; Génome Québec; Canadian Field Crop Research Alliance; Genome Canada; Syngenta Canada; Government of Canada; Ministère de l'Économie, de la Science et de l'Innovation - Québec; National Science Foundation","keywords":"Biology; Copy-number variation; Genotyping; Genetics; Comparative genomic hybridization; Population; Mutant; Genome; Gene duplication; Structural variation; Mutation; Computational biology; Genotype; 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":[0.004168997,0.001013002,0.0009078636,0.0007014705,0.0003494308,0.0009249821,0.001167877,0.001092532,0.001144078],"category_scores_gemma":[0.002251239,0.0005009336,0.0008466041,0.0002945502,0.0006910668,0.0005497359,0.0009608887,0.001664602,0.0006464974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437945,"about_ca_system_score_gemma":0.0006977704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118107,"about_ca_topic_score_gemma":0.001978137,"domain_scores_codex":[0.9978677,0.0004268469,0.0001598297,0.0005191047,0.000886513,0.000139813],"domain_scores_gemma":[0.9980287,0.0007286745,0.0003655923,0.000329822,0.0003644533,0.0001828],"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":[0.00006696041,0.0000803092,0.00118926,0.0001378526,0.00003620483,0.00009792807,0.00005391674,0.0009321808,0.9847296,0.0003697054,0.0002346036,0.01207156],"study_design_scores_gemma":[0.0001018435,0.0009713155,0.007172452,0.00004679052,0.0001441434,0.001074487,0.00006990095,0.02003483,0.9608551,0.0006176233,0.008830301,0.00008129185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.311543,0.001091541,0.6762237,0.0008309839,0.0002145903,0.001947024,0.002578191,0.003563816,0.002007169],"genre_scores_gemma":[0.3872568,0.001354622,0.603363,0.0005028324,0.00004134974,0.001016525,0.003787392,0.0006423029,0.002035197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004168997,"threshold_uncertainty_score":0.02204806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04270920781036244,"score_gpt":0.2750287823612322,"score_spread":0.2323195745508697,"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."}}