{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003910627,0.00008893874,0.0001096743,0.000006771875,0.0005883129,0.00007442791,0.0002231566,0.00004640977,0.00001166835],"category_scores_gemma":[0.00004782588,0.00003735724,0.0000558735,0.00002393231,0.0002260629,0.00007559833,0.00002155641,0.00003494563,0.000001481105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002201376,"about_ca_system_score_gemma":0.00000640506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001839082,"about_ca_topic_score_gemma":0.0006135539,"domain_scores_codex":[0.9993472,0.00002977287,0.0002032097,0.0001719516,0.0001038818,0.000144024],"domain_scores_gemma":[0.9995793,0.0001180733,0.000157374,0.00008695138,0.00002408424,0.00003426933],"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":[0.0001058684,0.00002737678,0.0389454,0.00003125537,0.00002723591,1.170364e-7,0.001853381,0.00005624451,0.940915,0.00006491628,0.0000944585,0.01787874],"study_design_scores_gemma":[0.000268171,0.0001063055,0.9703069,0.00000837432,0.00007477717,0.000003795366,0.00378482,0.0003275579,0.02027129,0.0004255842,0.004333843,0.0000886102],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966902,0.0005709018,0.0008562057,0.001306618,0.00009558945,0.000339317,0.00005234187,0.000007578049,0.00008121658],"genre_scores_gemma":[0.9988707,0.0003197833,0.0004506626,0.00003845093,0.00009070315,0.00003534336,0.00001690509,0.00000146901,0.0001759833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9313615,"threshold_uncertainty_score":0.4524887,"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."}}