{"id":"W3133931223","doi":"10.1594/pangaea.913907","title":"Metadata and NCBI-Accession numbers of 16S data for seawater, sediment, biofilm, and Vazella pourtalesii sponges from the Scotian Shelf (Canada) in summer 2016 and 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; Sediment; Accession; Oceanography; Seawater; Accession number (library science); Fishery; Geography; World Wide Web; Biology; Geology; Business; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009325245,0.00121974,0.001136621,0.006655876,0.001913516,0.001592263,0.001418874,0.0008377184,0.01393786],"category_scores_gemma":[0.003371815,0.0005212176,0.0007280161,0.01385284,0.0007647375,0.0005888302,0.001442296,0.0009919254,0.01007569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006595559,"about_ca_system_score_gemma":0.0246936,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7677089,"about_ca_topic_score_gemma":0.8658795,"domain_scores_codex":[0.9991513,0.00004042529,0.0001215238,0.0002134661,0.0002434765,0.0002298082],"domain_scores_gemma":[0.9973179,0.000308991,0.0003267161,0.0003062226,0.001333376,0.0004069372],"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":[0.0007374526,0.00006045816,0.04768108,0.008095444,0.0003707791,0.0005650203,0.001058589,0.001310644,0.006399039,0.002292125,0.90817,0.02325936],"study_design_scores_gemma":[0.000121069,0.00002139747,0.09451285,0.001045913,0.0001629437,0.0001787487,0.0005379127,0.0002794007,0.001475084,0.0006671938,0.9009361,0.00006149981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000899636,0.0001208586,0.00004988162,0.00001650825,0.000007135809,0.000008881278,0.9983886,0.00007406088,0.0004344793],"genre_scores_gemma":[0.001561968,0.0001351593,0.0002751334,0.00001626753,0.000001895145,0.00002888882,0.997534,0.00002171022,0.0004249008],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2322911,"threshold_uncertainty_score":0.4673182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09872129538845689,"score_gpt":0.3163097125163096,"score_spread":0.2175884171278527,"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."}}