{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001920108,0.0002044888,0.0001181195,0.001413813,0.000637409,0.000442873,0.0002765995,0.0002433006,0.0004798534],"category_scores_gemma":[0.0005428541,0.0001341397,0.0001296642,0.000957278,0.0003779664,0.0001265599,0.0004916841,0.0001380729,0.00008742493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009683652,"about_ca_system_score_gemma":0.0008442799,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2011042,"about_ca_topic_score_gemma":0.5197253,"domain_scores_codex":[0.9997866,0.00002805766,0.00001732785,0.00008354444,0.0000462504,0.00003827669],"domain_scores_gemma":[0.9995473,0.00005865542,0.0001401021,0.00002909958,0.0001435922,0.00008126028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000475284,0.00001130893,0.9785783,0.00003097245,0.00003487351,0.0002152236,0.001330647,0.0000779797,0.01315701,0.00003750158,0.0001373192,0.006341346],"study_design_scores_gemma":[0.000004401076,0.00001650845,0.9978459,0.000007321936,0.00001380083,0.0001264634,0.0006350846,0.0002129751,0.0004185527,0.00001499731,0.0007011665,0.000002759429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999003,0.00009333379,0.0001500343,0.00002183387,0.000001007327,0.00001125635,0.0002204983,0.000006190731,0.0004929571],"genre_scores_gemma":[0.9978593,0.00007570634,0.0009222763,0.00004219809,0.000001924771,0.00001630407,0.0005696742,0.000002849908,0.0005098603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2011042,"threshold_uncertainty_score":0.3998674,"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."}}