{"id":"W2616925360","doi":"10.1186/s12864-017-3780-9","title":"Transcriptomic resources for the medicinal legume Mucuna pruriens: de novo transcriptome assembly, annotation, identification and validation of EST-SSR markers","year":2017,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Directorate for Biological Sciences; Department of Biotechnology, Government of West Bengal; Department of Biotechnology, Ministry of Science and Technology, India; National Science Foundation","keywords":"Biology; Mucuna pruriens; Germplasm; Expressed sequence tag; Transcriptome; Genetics; De novo transcriptome assembly; Microsatellite; Population; Sequence assembly; Computational biology; Gene; Botany; Allele; Genome; Gene expression","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":[0.0007655796,0.001409793,0.001001437,0.001682528,0.0009141763,0.0009832344,0.0008426969,0.000512938,0.002760381],"category_scores_gemma":[0.001641374,0.000557052,0.001634292,0.002521147,0.0002590085,0.0007421626,0.0009584589,0.001090033,0.00260073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006057537,"about_ca_system_score_gemma":0.001185614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844801,"about_ca_topic_score_gemma":0.003875186,"domain_scores_codex":[0.999495,0.00005168151,0.00006828187,0.0001809444,0.0001329274,0.00007113523],"domain_scores_gemma":[0.998982,0.0002554488,0.0002114699,0.0001684195,0.0002599047,0.0001228005],"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":[0.0008313927,0.0001048376,0.005702189,0.001349071,0.0001138737,0.0008032488,0.0005372333,0.001628398,0.9556396,0.0003523934,0.002304954,0.03063268],"study_design_scores_gemma":[0.0003239933,0.001492731,0.2443437,0.0006917093,0.001469259,0.004482077,0.001180727,0.02661056,0.5733939,0.002281162,0.1434324,0.0002977394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6013315,0.005135894,0.1627292,0.0008218269,0.0001859981,0.0008293369,0.214835,0.008505085,0.005626174],"genre_scores_gemma":[0.2268145,0.002774511,0.2156579,0.000228304,0.00009567688,0.0008828392,0.5479761,0.001693395,0.003876699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002760381,"threshold_uncertainty_score":0.009234369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0287651193936784,"score_gpt":0.238023189367912,"score_spread":0.2092580699742336,"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."}}