{"id":"W2567624155","doi":"10.1139/gen-2016-0100","title":"The importance of molecular markers and primer design when characterizing biodiversity from environmental DNA","year":2016,"lang":"en","type":"article","venue":"Genome","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Environmental DNA; Biology; Biodiversity; Metagenomics; Primer (cosmetics); Computational biology; Ecology; Evolutionary biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001663603,0.0001415276,0.00012599,0.00001152588,0.0002576794,0.00001108133,0.0002718058,0.00004400542,0.000943178],"category_scores_gemma":[0.000008771878,0.000093879,0.00004598665,0.00002688872,0.0008665389,0.0001491833,0.0007120422,0.00004420601,0.0004274764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001778649,"about_ca_system_score_gemma":0.000001190072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000438542,"about_ca_topic_score_gemma":0.000003732308,"domain_scores_codex":[0.9989944,0.00006044822,0.0001445644,0.000322995,0.0002435003,0.000234053],"domain_scores_gemma":[0.9994407,0.00009452343,0.0001184569,0.0002726732,8.128433e-7,0.00007285352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002173976,0.00001272725,0.5102066,5.228222e-7,0.00002837857,0.000004388823,0.0001917321,5.532216e-7,0.4886431,7.952628e-7,0.000104532,0.0007849091],"study_design_scores_gemma":[0.0002592588,0.00004693574,0.9611222,0.000003608166,0.00002657737,0.000001133867,0.0001822278,5.520861e-7,0.03451665,0.000132359,0.003570181,0.0001383046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982284,0.0003594592,0.000172171,0.0005822261,0.0000413499,0.0002091354,0.0001762719,0.00001181952,0.0002191265],"genre_scores_gemma":[0.9957557,0.0006267413,0.003185916,0.0002515105,0.00000788169,0.000004399386,0.000006870442,0.000006233292,0.0001547343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4541264,"threshold_uncertainty_score":0.9999701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075075833092666,"score_gpt":0.1658915401924602,"score_spread":0.1551407818615335,"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."}}