{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002914862,0.0002161334,0.000160917,0.003534511,0.001271109,0.0005182583,0.0004675682,0.0002389982,0.001275683],"category_scores_gemma":[0.001217264,0.0001037531,0.0001763626,0.003412336,0.0003743998,0.000296744,0.0005802136,0.0002372409,0.0001514222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005392004,"about_ca_system_score_gemma":0.004616552,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9008495,"about_ca_topic_score_gemma":0.9664847,"domain_scores_codex":[0.9994968,0.00002103307,0.00001267568,0.0001053244,0.0002442133,0.0001199013],"domain_scores_gemma":[0.9992551,0.00006302227,0.000121329,0.00003027117,0.0004440694,0.00008615808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001433214,0.00002189873,0.8436022,0.0001529408,0.00008449305,0.0003311174,0.00357732,0.0008111739,0.0480626,0.001020891,0.001669175,0.1005229],"study_design_scores_gemma":[0.000002616049,0.00002090907,0.985769,0.00002124203,0.00002513424,0.0001545079,0.001324739,0.0008988201,0.002915007,0.00009981233,0.008753048,0.00001515425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887154,0.0006090946,0.002350536,0.0001654274,0.000005683853,0.00004254313,0.003006349,0.00003304166,0.005071918],"genre_scores_gemma":[0.990542,0.0004655973,0.005209609,0.00008303038,0.000002820405,0.00002205769,0.002106914,0.000009226329,0.001558799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09915048,"threshold_uncertainty_score":0.1994687,"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."}}