{"id":"W3107486053","doi":"10.1594/pangaea.917599","title":"Metadata and NCBI-Accession numbers of Vazella pourtalesii metagenomes from the Scotian Shelf (Canada) in summer 2017","year":2020,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020","keywords":"Metadata; Accession; Geography; Accession number (library science); Database; World Wide Web; Biology; Business; Computer science; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001967702,0.0003145366,0.0003817495,0.000111344,0.0007674178,0.0009517186,0.001479459,0.0002612781,0.00003058093],"category_scores_gemma":[0.000443929,0.0002643959,0.00007263513,0.000204056,0.0008674997,0.0002404711,0.002326455,0.000375674,0.000001532757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003636064,"about_ca_system_score_gemma":0.000222976,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8068238,"about_ca_topic_score_gemma":0.906809,"domain_scores_codex":[0.9970604,0.0002023843,0.0005372324,0.001215606,0.0004745258,0.0005098057],"domain_scores_gemma":[0.9978616,0.0001570013,0.0002795067,0.001401585,0.00004909292,0.0002511965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000995222,0.00006364581,0.0008695134,0.0001513421,0.0001701165,0.000001338012,0.00002187763,0.00001368612,0.001644249,0.0001056112,0.9949256,0.001933549],"study_design_scores_gemma":[0.0005458813,0.00004678872,0.00327165,0.00004254282,0.0001380169,0.000004662499,0.0002314011,0.0002153827,0.0003771512,0.00008208713,0.9947624,0.0002821027],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007201607,0.004804388,0.0003427912,0.001071943,0.0007958116,0.0008851223,0.9848735,0.00000430057,0.00002048888],"genre_scores_gemma":[0.009156906,0.007446866,0.0007204658,0.0002027046,0.0004040636,0.00006058322,0.9811779,0.00003248289,0.0007979722],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0999852,"threshold_uncertainty_score":0.9999808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08858275111853468,"score_gpt":0.3096593038183385,"score_spread":0.2210765526998038,"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."}}