{"id":"W3021407037","doi":"10.1002/aps3.11345","title":"A two‐tier bioinformatic pipeline to develop probes for target capture of nuclear loci with applications in Melastomataceae","year":2020,"lang":"en","type":"article","venue":"Applications in Plant Sciences","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Indian Institute of Science Education and Research Mohali; University of Colombo; Natural Sciences and Engineering Research Council of Canada; Universidade Estadual Paulista; Indian Institute of Science Education and Research Thiruvananthapuram; Society for the Study of Evolution; Botanical Society of America; National University of Singapore; Indian Institute of Science; University of Florida; Society of Systematic Biologists; Florida Museum of Natural History; American Society of Plant Taxonomists; Inyuvesi Yakwazulu-Natali; National Science Foundation","keywords":"Melastomataceae; Biology; Pipeline (software); Modularity (biology); Computational biology; Evolutionary biology; Botany; Computer science; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001853612,0.001499877,0.001035641,0.001403738,0.0009091417,0.001079868,0.001208741,0.00105233,0.005314847],"category_scores_gemma":[0.00287373,0.001264532,0.001453082,0.0008671696,0.0005006904,0.001078218,0.001603562,0.001990185,0.003396064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006454238,"about_ca_system_score_gemma":0.001326459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280671,"about_ca_topic_score_gemma":0.002883468,"domain_scores_codex":[0.9986551,0.0001466795,0.0001015834,0.0006517997,0.0002764146,0.0001684577],"domain_scores_gemma":[0.9989083,0.0004700874,0.0001843478,0.000158459,0.0001828423,0.00009609108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004544954,0.0001260638,0.004653814,0.0005985025,0.0001101622,0.0002128916,0.0004155421,0.001524971,0.9330478,0.001053786,0.003575018,0.05422699],"study_design_scores_gemma":[0.0002616776,0.0005939887,0.03292328,0.00008139954,0.0002814531,0.001136317,0.0002534574,0.07942977,0.832646,0.003247751,0.04892975,0.0002152079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1230642,0.0006451784,0.827424,0.0005245871,0.0001184658,0.001226805,0.01084222,0.03319593,0.00295857],"genre_scores_gemma":[0.1341061,0.0002099366,0.840524,0.0009303219,0.00003398894,0.001729316,0.01527575,0.002486056,0.004704549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005314847,"threshold_uncertainty_score":0.01777995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956495004714514,"score_gpt":0.226430569047449,"score_spread":0.2068656190003039,"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."}}