{"id":"W2069236666","doi":"10.1371/journal.pone.0085019","title":"An Example of How Barcodes Can Clarify Cryptic Species: The Case of the Calanoid Copepod Mastigodiaptomus albuquerquensis (Herrick)","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Lepidoptera: Biology and Taxonomy","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Environmental Biology; University of Texas at El Paso; Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Gobierno de México; National Science Foundation; Consejo Nacional de Ciencia y Tecnología; Ontario Genomics Institute; Genome Canada; Ontario Genomics","keywords":"Subspecies; Species complex; Biology; Nearctic ecozone; Species richness; Range (aeronautics); Ecology; Zoology; Species description; Plateau (mathematics); Taxonomy (biology); Genus; Evolutionary biology; Phylogenetic tree","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":[0.005965834,0.0006344824,0.0004719149,0.0026113,0.002913372,0.002193672,0.001118998,0.003652391,0.002381207],"category_scores_gemma":[0.01190867,0.0003231492,0.0005681433,0.002021438,0.003897369,0.00374011,0.001334874,0.002577195,0.0008927534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179661,"about_ca_system_score_gemma":0.0009071727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02078981,"about_ca_topic_score_gemma":0.02630591,"domain_scores_codex":[0.9980028,0.001000685,0.00007331183,0.0002730685,0.0004573656,0.0001927513],"domain_scores_gemma":[0.9900728,0.005507282,0.001109099,0.0008894612,0.00210262,0.0003186425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007080855,0.0004027122,0.2182262,0.001012615,0.0004091286,0.02687332,0.0296925,0.006057463,0.08451282,0.08322591,0.04017663,0.5087026],"study_design_scores_gemma":[0.0001030433,0.0006189132,0.213936,0.001704806,0.0003412579,0.02993102,0.02808908,0.05286999,0.06611136,0.1350723,0.4705766,0.0006456912],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.531499,0.0104113,0.3339639,0.04583678,0.001593429,0.0004206183,0.001323536,0.00226464,0.07268678],"genre_scores_gemma":[0.5910997,0.001696681,0.3956932,0.003788315,0.0002065696,0.0001178131,0.0004031604,0.000346163,0.006648419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02078981,"threshold_uncertainty_score":0.04133761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03661199014915791,"score_gpt":0.2112363455511796,"score_spread":0.1746243554020217,"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."}}