{"id":"W2550678220","doi":"10.1371/journal.pone.0166118","title":"Exploring Canadian Echinoderm Diversity through DNA Barcodes","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Marine Biology and Ecology Research","field":"Earth and Planetary Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Nuclear Safety Commission; Government of Canada; Genome Canada; Ontario Genomics; Ontario Genomics Institute","keywords":"Echinoderm; DNA barcoding; Biology; Invertebrate; Marine invertebrates; Barcode; Biological dispersal; Crustacean; Taxonomic rank; Ecology; Environmental DNA; Evolutionary biology; Zoology; Biodiversity; Taxon","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001818174,0.00006776922,0.0001100402,0.00005719468,0.0004772782,0.000009316428,0.0002330112,0.00005980598,0.01033151],"category_scores_gemma":[0.00009564143,0.0000454476,0.00002114796,0.00006984496,0.0001100271,0.000319793,0.00005611174,0.00009530119,0.00181027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001057082,"about_ca_system_score_gemma":0.00006397185,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1483919,"about_ca_topic_score_gemma":0.6331064,"domain_scores_codex":[0.9991676,0.00005976568,0.00006740936,0.0001838546,0.0001199727,0.0004013575],"domain_scores_gemma":[0.9995018,0.0001453391,0.00001409974,0.0001349847,0.00002395251,0.0001798242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001651157,0.0000233585,0.9961516,0.000006617903,0.00004716553,0.00001881362,0.0001133524,4.972014e-7,0.0001162232,0.00005904805,0.0001322374,0.003314602],"study_design_scores_gemma":[0.0001344583,0.00006834732,0.9966558,0.00001421841,0.00001071911,0.000001465254,0.00002872865,0.00004217242,0.001408416,0.001015593,0.0005324062,0.00008771516],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9739681,0.00003970231,0.000002499611,0.001889071,0.00006595717,0.00007667577,0.00003729453,0.00003052182,0.02389014],"genre_scores_gemma":[0.9978241,0.0002507916,0.0001773486,0.0002334968,0.00008810452,0.000001448197,0.00001693939,0.000001433257,0.001406281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4847145,"threshold_uncertainty_score":0.9989669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2754655034869926,"score_gpt":0.221069135897709,"score_spread":0.05439636758928354,"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."}}