{"id":"W1957133460","doi":"10.1111/1755-0998.12468","title":"Development of highly reliable in silico SNP resource and genotyping assay from exome capture and sequencing: an example from black spruce (<i>Picea mariana</i>)","year":2015,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université Laval","funders":"Canada Foundation for Innovation; Fonds de Recherche du Québec - Santé; Genome Canada; McGill University","keywords":"Biology; In silico; Genotyping; SNP genotyping; Population; Genetics; Single-nucleotide polymorphism; Genomics; Exome; Computational biology; Exome sequencing; Population genomics; SNP; Molecular Inversion Probe; Genome; Genotype; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000923548,0.0006102421,0.0005287814,0.0005453421,0.0004357275,0.0008223456,0.0004732327,0.0006738318,0.0006373379],"category_scores_gemma":[0.001300527,0.0003319403,0.0006707074,0.0004169263,0.0002470658,0.0002552149,0.0005843301,0.0006607714,0.0007281423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001839876,"about_ca_system_score_gemma":0.0003252392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413784,"about_ca_topic_score_gemma":0.00365641,"domain_scores_codex":[0.9993777,0.00007987985,0.00005937877,0.0002235727,0.0002073658,0.00005204086],"domain_scores_gemma":[0.9994639,0.000181574,0.00006658765,0.00009240919,0.0001321924,0.00006329899],"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":[0.0001362112,0.00004875267,0.009810233,0.0001928649,0.00006321556,0.0005451239,0.0002055914,0.00115316,0.9685573,0.0002400894,0.000502396,0.01854493],"study_design_scores_gemma":[0.00004724909,0.0005293583,0.1000339,0.0000826339,0.0003135447,0.003652947,0.0002549441,0.02387774,0.8438693,0.0005942615,0.02663078,0.0001132699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8021523,0.0008530469,0.1847275,0.0003627683,0.0001016748,0.000411362,0.005879261,0.002472369,0.003039661],"genre_scores_gemma":[0.589998,0.00074208,0.3856078,0.0004128256,0.00003104241,0.0003832571,0.01816016,0.0005089955,0.004155803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001413784,"threshold_uncertainty_score":0.004884243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784145108213748,"score_gpt":0.2231211170669267,"score_spread":0.1952796659847892,"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."}}