{"id":"W2101019506","doi":"10.1186/1471-2164-8-424","title":"High throughput SNP discovery and genotyping in grapevine (Vitis vinifera L.) by combining a re-sequencing approach and SNPlex technology","year":2007,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":275,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario; Genome Canada","keywords":"Biology; Genetics; SNP genotyping; Genotyping; Single-nucleotide polymorphism; Molecular Inversion Probe; DNA sequencing; Genotype; Genetic diversity; SNP; Reference genome; Coding region; Genome; Whole genome sequencing; Computational biology; Gene; Population","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004541892,0.0002046883,0.0002934143,0.0000373962,0.000257526,0.0001678345,0.0001890465,0.0001891685,0.00001376855],"category_scores_gemma":[0.0001214438,0.00008525044,0.00004012551,0.0004484331,0.0001937119,0.0002710999,0.0002476711,0.0002750962,0.000003433707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154465,"about_ca_system_score_gemma":0.00001612659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009192458,"about_ca_topic_score_gemma":0.004120131,"domain_scores_codex":[0.9983928,0.00004903746,0.0003427933,0.0004815171,0.0001607987,0.0005730169],"domain_scores_gemma":[0.9994456,0.0002428832,0.00007835742,0.00006431356,0.00004337062,0.000125444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000546684,0.00004776847,0.0222932,0.00004029382,0.00001240677,0.00001391771,0.0003858121,0.000009766856,0.9440157,0.001631151,0.00007900171,0.03141624],"study_design_scores_gemma":[0.002263132,0.00139281,0.8486633,0.0002802927,0.00006127715,0.0003767649,0.0337755,0.001282378,0.0959044,0.006486014,0.007424917,0.002089247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972944,0.001280706,0.0000924093,0.0006596589,0.00003819934,0.0002997241,0.00002521429,0.00005051833,0.000259142],"genre_scores_gemma":[0.996865,0.0005950119,0.002034268,0.0001152255,0.00009776504,0.00001411296,0.00007075947,0.000002427383,0.0002054421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8481114,"threshold_uncertainty_score":0.3476412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04708502102174326,"score_gpt":0.2619574883532993,"score_spread":0.214872467331556,"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."}}