{"id":"W2740731509","doi":"10.1371/journal.pone.0182283","title":"DNA barcoding of odonates from the Upper Plata basin: Database creation and genetic diversity estimation","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum; University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Rufford Foundation","keywords":"DNA barcoding; Biology; Intraspecific competition; Biodiversity; Interspecific competition; Ecology; Zoology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009802594,0.00005394437,0.00006550877,0.00001361105,0.0004479069,0.0000563021,0.000216314,0.00004247635,0.00003789199],"category_scores_gemma":[0.0003104325,0.00004699223,0.0000179005,0.00001404481,0.0001184213,0.00001243305,0.0002357826,0.00003145387,0.000009319816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003398257,"about_ca_system_score_gemma":0.00001117881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001112596,"about_ca_topic_score_gemma":0.0000192188,"domain_scores_codex":[0.9995106,0.00003017338,0.0001111085,0.0001735656,0.0001184243,0.00005606364],"domain_scores_gemma":[0.9991809,0.00002529044,0.0001423057,0.000544237,0.00008325297,0.00002405976],"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":[0.00002502218,0.0001221774,0.1106497,0.00002225414,0.00008186742,1.579078e-7,0.0001139297,0.000002662677,0.8874716,0.0002547386,0.0003691937,0.0008867024],"study_design_scores_gemma":[0.0001768147,0.00001440943,0.4279374,0.00003042313,0.00006344735,3.260175e-7,0.00003536271,0.001078982,0.5704052,0.00007054176,0.0001349255,0.00005214418],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997311,0.0001421034,0.001301439,0.0007296149,0.00003019393,0.0001164649,0.0001489685,0.000004921655,0.0002153125],"genre_scores_gemma":[0.9960485,0.0002304133,0.002935775,0.00005384075,0.00005090678,0.000005005177,0.0004567338,0.000005204087,0.0002136344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3172877,"threshold_uncertainty_score":0.3444983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06484008518690773,"score_gpt":0.2647489603584649,"score_spread":0.1999088751715572,"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."}}