{"id":"W3023213195","doi":"10.1007/978-1-4939-3774-5_10","title":"DNA Barcoding of Marine Metazoans","year":2016,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Government of Canada; Ontario Genomics Institute; Genome Canada","keywords":"DNA barcoding; Phylum; Biology; Barcode; Evolutionary biology; Identification (biology); DNA sequencing; Computational biology; DNA; Paleontology; Ecology; Computer science; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003027075,0.0002764419,0.0003649603,0.001283461,0.0006279892,0.0008251164,0.0006493624,0.0007012172,0.001058339],"category_scores_gemma":[0.001770385,0.0003031459,0.0003481843,0.00118542,0.0005783378,0.0005813069,0.0007859191,0.001013084,0.001001072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006975061,"about_ca_system_score_gemma":0.001030835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003670144,"about_ca_topic_score_gemma":0.005309649,"domain_scores_codex":[0.9995462,0.00003254019,0.00002961203,0.000183743,0.0001635608,0.00004419494],"domain_scores_gemma":[0.9990863,0.0002823743,0.0002344097,0.000136797,0.0001932425,0.00006685896],"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":[0.0001352906,0.00001533718,0.006067714,0.0003531456,0.00002404002,0.0001367517,0.0005567959,0.0006427307,0.9190312,0.004817326,0.0006875375,0.06753219],"study_design_scores_gemma":[0.00002029278,0.0002452388,0.1088092,0.0004956172,0.0001441128,0.0008571491,0.0007390458,0.009127965,0.7768396,0.009289859,0.09335061,0.00008123432],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8068182,0.01259276,0.1473053,0.001530839,0.0007015719,0.0001096717,0.01844777,0.001334608,0.01115929],"genre_scores_gemma":[0.8831344,0.005025464,0.08794179,0.000865189,0.0002678281,0.0001526662,0.01200665,0.0003003824,0.01030553],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003670144,"threshold_uncertainty_score":0.007297575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03604365331397369,"score_gpt":0.3906385149175332,"score_spread":0.3545948616035595,"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."}}