{"id":"W2089104593","doi":"10.1371/journal.pone.0125635","title":"The Hemiptera (Insecta) of Canada: Constructing a Reference Library of DNA Barcodes","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Fossil Insects in Amber","field":"Agricultural and Biological Sciences","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Genome Canada; Ontario Genomics; National Museum of Natural History; Ontario Genomics Institute","keywords":"Barcode; DNA barcoding; Workflow; Context (archaeology); Hemiptera; Identification (biology); Biology; GenBank; Taxonomy (biology); DNA sequencing; Checklist; Computer science; Library science; Computational biology; World Wide Web; Information retrieval; Zoology; Database; DNA; Ecology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001191667,0.00006441709,0.0001428745,0.000003272457,0.00006435706,0.00001569412,0.0002515524,0.0000352696,0.0001054418],"category_scores_gemma":[0.000232262,0.00002074649,0.00001777604,0.0001466981,0.0001496704,0.00008484902,0.00008094014,0.00008287326,0.000001310067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001447597,"about_ca_system_score_gemma":0.0001002109,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03613771,"about_ca_topic_score_gemma":0.1077233,"domain_scores_codex":[0.9992058,0.00005083733,0.0001885479,0.0001042752,0.0002982741,0.000152261],"domain_scores_gemma":[0.999329,0.0003325783,0.0001192933,0.00006435739,0.00008574747,0.00006905721],"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":[0.00007346344,0.0001782616,0.04964975,0.00003754025,0.00006635431,0.000003509639,0.000160866,7.502116e-7,0.9380609,0.00186006,0.002401123,0.007507436],"study_design_scores_gemma":[0.0001432548,0.0002261547,0.03793307,0.000193601,0.00002001536,0.000004705123,0.0009249505,0.00005747731,0.9572292,0.001507913,0.001604241,0.0001554343],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.974362,0.000230762,3.676671e-8,0.000583162,0.00002916819,0.00007115061,0.00003393009,0.00001746427,0.02467231],"genre_scores_gemma":[0.9989171,0.0000175303,0.0004699158,0.0000546649,0.00005610436,0.000003705227,0.000004436748,6.312135e-7,0.0004759281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07158563,"threshold_uncertainty_score":0.9702807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07563019280491053,"score_gpt":0.1957319176360875,"score_spread":0.120101724831177,"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."}}