{"id":"W2744601887","doi":"10.1038/s41598-017-09285-z","title":"Genome-wide Target Enrichment-aided Chip Design: a 66 K SNP Chip for Cashmere Goat","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Natural Science Foundation of China","keywords":"SNP; Single-nucleotide polymorphism; SNP genotyping; Biology; Genome; Chip; Computational biology; Genetics; SNP array; Genotype; Gene; Computer science; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001155798,0.0005088152,0.00056655,0.0004507317,0.0003056906,0.0003871056,0.0006002611,0.0005233004,0.0008991213],"category_scores_gemma":[0.0006680587,0.0003489825,0.0005144971,0.0002508269,0.0004595978,0.0002124672,0.0004980091,0.0003574164,0.0003789649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003215558,"about_ca_system_score_gemma":0.0005718744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005083,"about_ca_topic_score_gemma":0.002158514,"domain_scores_codex":[0.9994072,0.0001144636,0.00002658063,0.0001812018,0.0001827891,0.00008787261],"domain_scores_gemma":[0.9997659,0.00008147564,0.00003401999,0.00003523734,0.00005712727,0.00002628645],"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":[0.0001380388,0.00002931201,0.002250201,0.000104622,0.00003337922,0.0001038914,0.00005318227,0.001835276,0.9841455,0.0002951609,0.0002357008,0.01077565],"study_design_scores_gemma":[0.00008188626,0.0008651424,0.02006213,0.00001274239,0.000165875,0.0006223631,0.00007504415,0.03147514,0.9356042,0.0003725068,0.01057816,0.00008490789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6126927,0.001081328,0.3808219,0.000345037,0.0001024843,0.0005888727,0.001240704,0.001331435,0.001795411],"genre_scores_gemma":[0.5326711,0.000555486,0.4596865,0.0004407829,0.00003302236,0.0008579085,0.002124074,0.0001052583,0.003525875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001155798,"threshold_uncertainty_score":0.006112516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02944072076930681,"score_gpt":0.2735908677730369,"score_spread":0.2441501470037301,"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."}}