{"id":"W1995061033","doi":"10.1186/1471-2164-9-21","title":"Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce","year":2008,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université Laval; Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada","funders":"Genome Canada","keywords":"Biology; SNP genotyping; Genome; Genetics; Black spruce; Genotyping; Single-nucleotide polymorphism; Population; Candidate gene; Reference genome; Synteny; Computational biology; Gene; Genotype; Ecology","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":[0.001178217,0.000376328,0.000366501,0.0003761838,0.0002020188,0.0006726371,0.0004663772,0.0004091045,0.0008997117],"category_scores_gemma":[0.00112383,0.0003233542,0.0003646876,0.0003838963,0.0003025479,0.0002796518,0.0006065203,0.0005116055,0.0003232232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003446197,"about_ca_system_score_gemma":0.0002555122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005565796,"about_ca_topic_score_gemma":0.002002629,"domain_scores_codex":[0.999416,0.0001209962,0.00002804828,0.0002454942,0.0001235417,0.00006594752],"domain_scores_gemma":[0.9992849,0.0003016862,0.0001483343,0.000111022,0.00008831231,0.00006585346],"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":[0.0001514074,0.00008690208,0.005266811,0.0001216013,0.00005569716,0.000053947,0.0001028117,0.00684472,0.9508733,0.0005972767,0.0002080857,0.03563737],"study_design_scores_gemma":[0.00007519094,0.0004362796,0.03471558,0.00002543334,0.0001302793,0.0003445875,0.00005161341,0.08223406,0.870467,0.001570094,0.009882632,0.00006724573],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.632432,0.000645523,0.3625112,0.0001520999,0.0000396098,0.0002390063,0.001364941,0.001226896,0.001388789],"genre_scores_gemma":[0.5161285,0.000333925,0.4805301,0.0001308845,0.00002130722,0.0002695946,0.001143496,0.00009331841,0.001348969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001178217,"threshold_uncertainty_score":0.00623107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04063927764169348,"score_gpt":0.2433622628649373,"score_spread":0.2027229852232439,"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."}}