{"id":"W2484748210","doi":"10.1111/1755-0998.12570","title":"Exome capture from the spruce and pine giga‐genomes","year":2016,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Calgary","funders":"National Institute of Food and Agriculture; Alberta Innovates Bio Solutions; Ministry of Forests, Lands and Natural Resource Operations","keywords":"Biology; Genome; Exome; Evolutionary biology; Computational biology; Exome sequencing; Genetics; Gene; Mutation","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":[],"consensus_categories":[],"category_scores_codex":[0.0001247061,0.0001930902,0.0001974294,0.00002129542,0.0001579864,0.00002238595,0.0002797314,0.000173909,0.00003630392],"category_scores_gemma":[0.00007965908,0.0001121486,0.00007608751,0.00004305239,0.000326099,6.727457e-7,0.0003276453,0.00006538507,0.00001552939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007635476,"about_ca_system_score_gemma":0.00002186349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004769072,"about_ca_topic_score_gemma":0.0002294115,"domain_scores_codex":[0.9989299,0.00010829,0.0001646671,0.0004321504,0.0000761328,0.0002888588],"domain_scores_gemma":[0.9993339,0.00007075734,0.00007502586,0.0004062811,0.00004368812,0.000070381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004180586,0.00001844624,0.07553355,0.00000289355,0.0002102045,0.000014894,0.0002453445,0.000005903315,0.9196319,0.0001195571,0.001144032,0.003031487],"study_design_scores_gemma":[0.00143843,0.0004363115,0.410142,0.00001574375,0.0001133036,0.00005175074,0.0003928615,0.000005208144,0.0946158,0.002202924,0.4900774,0.000508249],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768195,0.0179434,0.0001394843,0.004016323,0.0001216453,0.0001616363,0.00006066864,0.000006065351,0.0007313461],"genre_scores_gemma":[0.9958754,0.00110086,0.0002495191,0.001808061,0.0002014343,0.00003122637,0.00001247596,0.00002510144,0.0006959567],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8250161,"threshold_uncertainty_score":0.4573289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004942711947257988,"score_gpt":0.1925625163095113,"score_spread":0.1876198043622533,"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."}}