{"id":"W2481569758","doi":"10.1139/gen-2016-0022","title":"DNA-based identification of invasive alien species in relation to Canadian federal policy and law, and the basis of rapid-response management","year":2016,"lang":"en","type":"article","venue":"Genome","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Research and Innovation","keywords":"Identification (biology); Legislation; Federal law; Agency (philosophy); Government (linguistics); DNA barcoding; Biology; Introduced species; Invasive species; Business; Law; Environmental resource management; Political science; Ecology; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.01988552,0.0005073903,0.0004067995,0.006170638,0.0135133,0.007964349,0.004881818,0.003514981,0.003916445],"category_scores_gemma":[0.04074527,0.0006804522,0.0004893332,0.003945543,0.009182182,0.00292865,0.00277838,0.004103675,0.0004055781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1360297,"about_ca_system_score_gemma":0.4444101,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.994768,"about_ca_topic_score_gemma":0.9970981,"domain_scores_codex":[0.9734426,0.002698194,0.001411573,0.001585796,0.01663807,0.004223886],"domain_scores_gemma":[0.9366892,0.009270291,0.003479125,0.001786391,0.04321583,0.005559176],"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":[0.0001092951,0.0002288143,0.06483603,0.0009164302,0.000063623,0.0009185408,0.01588585,0.004395429,0.007703076,0.3057751,0.3880409,0.211127],"study_design_scores_gemma":[0.00002914517,0.00005624318,0.09231538,0.001689314,0.0001021082,0.0001696055,0.009518349,0.004175256,0.003368937,0.02679295,0.8615049,0.0002777491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07180992,0.01327463,0.01525424,0.4536344,0.001634598,0.001395546,0.004488343,0.000507355,0.438001],"genre_scores_gemma":[0.6472506,0.01643477,0.07417697,0.101294,0.0005709305,0.0007703655,0.003802225,0.0002394776,0.1554607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1360297,"threshold_uncertainty_score":0.9869689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01023561888139974,"score_gpt":0.1912844362876391,"score_spread":0.1810488174062394,"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."}}