{"id":"W2983054701","doi":"10.1002/edn3.56","title":"Using environmental DNA metabarcoding to map invasive and native invertebrates in two Great Lakes tributaries","year":2019,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Trillium Foundation","keywords":"Environmental DNA; Dreissena; Introduced species; Biology; Ecology; Tributary; Invertebrate; Endangered species; Invasive species; Taxon; Genus; DNA barcoding; Freshwater ecosystem; Habitat; Geography; Biodiversity; Ecosystem; Mollusca; Bivalvia","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002806345,0.0006161783,0.000568115,0.0001639641,0.0003791266,0.0000705978,0.0004196817,0.0001128021,0.005085711],"category_scores_gemma":[0.0000252374,0.0006385127,0.0001233417,0.0001960844,0.0009437875,0.0009010084,0.001995975,0.0003117938,0.004088375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00157306,"about_ca_system_score_gemma":0.000004669761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004393866,"about_ca_topic_score_gemma":0.0003559581,"domain_scores_codex":[0.9966553,0.0001718625,0.000456816,0.001180273,0.0007233678,0.0008123175],"domain_scores_gemma":[0.9989365,0.0001705109,0.0001580638,0.0004223754,7.025461e-7,0.0003118979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004497492,0.0001313551,0.6964541,0.000009992552,0.00004631821,0.00003232381,0.001907354,0.0005036074,0.3002975,0.00001638589,0.0001454472,0.000410588],"study_design_scores_gemma":[0.002335913,0.0002616204,0.8682269,0.00007004535,0.00009091493,0.00003544852,0.006595966,0.0005552873,0.1171436,0.0002683352,0.003212125,0.001203819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964664,0.0003146784,0.00001820978,0.0001934037,0.0002024409,0.001103412,0.0002837887,0.00004041573,0.001377257],"genre_scores_gemma":[0.9945758,0.0002062924,0.00330002,0.000717278,0.00003328727,0.0000373189,0.00007339528,0.00005133636,0.001005297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.183154,"threshold_uncertainty_score":0.9996066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728929857792769,"score_gpt":0.2249329248267132,"score_spread":0.2076436262487855,"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."}}