{"id":"W3042248073","doi":"10.1111/ddi.13108","title":"The risks of using molecular biodiversity data for incidental detection of species of concern","year":2020,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Biosecurity; Biodiversity; Threatened species; Interim; Identification (biology); Data quality; Environmental resource management; Quality (philosophy); Environmental planning; Business; Risk analysis (engineering); Ecology; Biology; Geography; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008549682,0.00005562471,0.0001044834,0.000006709207,0.0008387056,0.000003252492,0.0002810283,0.00002891097,0.00003340854],"category_scores_gemma":[0.00007825012,0.00005158032,0.00004660956,0.00009355586,0.001031031,0.0001241786,0.003433523,0.00003486135,0.000002552598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000497932,"about_ca_system_score_gemma":0.000001848278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001158987,"about_ca_topic_score_gemma":0.00003790293,"domain_scores_codex":[0.9995021,0.00002030297,0.00009786402,0.0001444171,0.0001544333,0.00008089795],"domain_scores_gemma":[0.9996264,0.00005774337,0.0001218764,0.0001440965,0.00001131173,0.00003855437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005782998,0.00003730879,0.956799,0.00001700957,0.00004496113,5.467907e-7,0.000452503,0.0000711278,0.04205878,0.00006459162,0.0002272818,0.0001690662],"study_design_scores_gemma":[0.0004420772,0.00009923609,0.9211097,0.000004648904,0.0001929941,5.190277e-7,0.003755277,0.0006129194,0.07274887,0.00006793402,0.0008804597,0.00008535894],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873782,0.000070359,0.006094115,0.0001039376,0.00002400784,0.0001376118,0.00614454,0.000004030958,0.00004315929],"genre_scores_gemma":[0.9994405,0.00008083704,0.0003850736,0.00001270252,0.000002744677,1.859334e-7,0.0000751577,6.63835e-7,0.000002165042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03568929,"threshold_uncertainty_score":0.6450731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1497728587317124,"score_gpt":0.274293911648075,"score_spread":0.1245210529163627,"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."}}