{"id":"W3213678768","doi":"10.1038/s43705-021-00070-8","title":"Paleo-diatom composition from Santa Barbara Basin deep-sea sediments: a comparison of <i>18S-V9</i> and <i>diat-rbcL</i> metabarcoding vs shotgun metagenomics","year":2021,"lang":"en","type":"article","venue":"ISME Communications","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Directorate for Biological Sciences; Université de Recherche Paris Sciences et Lettres; Muséum National d'Histoire Naturelle; European Commission; University of Tasmania; Australian Government; Agence Nationale de la Recherche; National Science Foundation","keywords":"Metagenomics; Diatom; Shotgun sequencing; Biology; Eukaryote; Ancient DNA; Compositional data; Ecology; DNA sequencing; DNA; Gene; Genetics; Genome","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002056133,0.0002268265,0.0004303755,0.00003954532,0.0006300946,0.00005039018,0.000776455,0.00008703061,0.0005666594],"category_scores_gemma":[0.00002813077,0.000259546,0.0001158153,0.000249854,0.0007385529,0.0003073052,0.002448996,0.0002679604,0.0001724566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001958306,"about_ca_system_score_gemma":0.000007588347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005549281,"about_ca_topic_score_gemma":0.0003554441,"domain_scores_codex":[0.9982547,0.0003064743,0.0004517831,0.0003991717,0.0003336427,0.0002542375],"domain_scores_gemma":[0.9979231,0.0004058372,0.0002319174,0.001297783,0.00001688749,0.0001244949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001643045,0.0004997227,0.9319932,0.00001083535,0.000173592,0.000002520727,0.002017414,0.0001747185,0.06205837,0.00009078786,0.001593442,0.001368974],"study_design_scores_gemma":[0.001002985,0.00005944697,0.9294811,0.0000471719,0.0005462928,0.000005964291,0.00260127,0.00321775,0.04075152,0.0003189378,0.0214792,0.0004882991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853224,0.006403651,0.0007214034,0.001472605,0.00008901805,0.000323256,0.0005385893,0.0000508004,0.005078262],"genre_scores_gemma":[0.9320051,0.004056793,0.06278449,0.0006538994,0.00001269229,0.00002381144,0.0004122912,0.00001668904,0.00003422272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06206309,"threshold_uncertainty_score":0.9999857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03203075402625161,"score_gpt":0.2638287686575644,"score_spread":0.2317980146313128,"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."}}