{"id":"W3010850328","doi":"10.1101/2020.03.16.993667","title":"The utility of a metagenomics approach for marine biomonitoring","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Metagenomics; Environmental DNA; Shotgun sequencing; Biodiversity; Biology; Computational biology; Ecosystem; DNA sequencing; Function (biology); Biobank; Ecology; Evolutionary biology; Bioinformatics; Gene; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001654564,0.0006999249,0.0004711247,0.002348844,0.0005044816,0.00140809,0.0004736829,0.0005954521,0.000780014],"category_scores_gemma":[0.001552622,0.00030249,0.0005473457,0.001268024,0.0004096692,0.0007636298,0.000994892,0.001059419,0.0002651153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000791363,"about_ca_system_score_gemma":0.0006165639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002779356,"about_ca_topic_score_gemma":0.004435658,"domain_scores_codex":[0.9994031,0.0002644779,0.00002570559,0.0001497598,0.000128372,0.00002851576],"domain_scores_gemma":[0.9993919,0.0002404269,0.00009904689,0.0001038365,0.0001125443,0.00005218877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003387315,0.0001934213,0.0877754,0.0004326717,0.000705683,0.0003292219,0.0002629266,0.01345505,0.7949769,0.005605455,0.0008675489,0.09505703],"study_design_scores_gemma":[0.00006907895,0.001009115,0.2489567,0.0002242944,0.0008653248,0.001266412,0.0008399303,0.2141545,0.4688756,0.03600406,0.02753681,0.0001981759],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4123228,0.007815617,0.5606549,0.003018605,0.000208595,0.000480406,0.005265797,0.001995981,0.008237246],"genre_scores_gemma":[0.6118511,0.002318793,0.3824945,0.0004052282,0.00005541859,0.0002289422,0.001193779,0.0001436399,0.001308567],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002779356,"threshold_uncertainty_score":0.00875026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02641091314549722,"score_gpt":0.2134820079269893,"score_spread":0.1870710947814921,"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."}}