{"id":"W2338023675","doi":"10.1139/gen-2015-0220","title":"Priming of a DNA metabarcoding approach for species identification and inventory in marine macrobenthic communities","year":2016,"lang":"en","type":"article","venue":"Genome","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biology; Environmental DNA; Species richness; Biodiversity; Phylum; Ecology; Pyrosequencing; Species diversity; Operational taxonomic unit; Species identification; Identification (biology); Taxonomic rank; Species description; Evolutionary biology; Taxonomy (biology); Taxon; 16S ribosomal RNA; Genetics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002631453,0.00007256315,0.0001128132,0.00004339405,0.00009624197,0.000007353349,0.0001381046,0.00002348331,0.0001690062],"category_scores_gemma":[0.00001280543,0.00005971426,0.00002530719,0.00005021407,0.0003799226,0.0001361482,0.0004962956,0.00003015531,0.00001323033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001307761,"about_ca_system_score_gemma":8.390755e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001014787,"about_ca_topic_score_gemma":0.0000460851,"domain_scores_codex":[0.9994587,0.00003026483,0.0001500094,0.0001269268,0.0001047129,0.0001294284],"domain_scores_gemma":[0.9997258,0.0000433927,0.00007175619,0.0001360833,0.000002120853,0.00002091406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009589719,0.00004086574,0.6014861,0.00003773739,0.00001171048,1.791213e-7,0.002203908,0.000019328,0.3951102,0.00006968523,0.00001455859,0.000996129],"study_design_scores_gemma":[0.0002873728,0.00002144084,0.9863152,0.00001006099,0.00001308106,7.511521e-7,0.002431059,0.00001805716,0.009930873,0.0001782546,0.000705953,0.00008793012],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975435,0.00009157533,0.0006782311,0.00007132634,0.00001840797,0.0002282418,0.00002546512,0.000007534565,0.001335711],"genre_scores_gemma":[0.9928721,0.0002644842,0.005831883,0.00001898509,0.000006338552,0.0000203486,0.00001007495,0.000004936738,0.0009709104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3851794,"threshold_uncertainty_score":0.2435077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04030363561949089,"score_gpt":0.212911117219075,"score_spread":0.1726074815995841,"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."}}