{"id":"W3023820793","doi":"10.3897/neobiota.56.47475","title":"Monitoring the silver carp invasion in Africa: a case study using environmental DNA (eDNA) in dangerous watersheds","year":2020,"lang":"en","type":"article","venue":"NeoBiota","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":26,"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; National Science Foundation","keywords":"Environmental DNA; Biodiversity; Silver carp; Electrofishing; Biosecurity; Invasive species; Fishery; Hypophthalmichthys; Introduced species; Ecology; Habitat; Environmental science; Netting; Geography; Biology; Fish <Actinopterygii>","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002181899,0.0002415324,0.0002152748,0.00003881193,0.0002540804,0.00003262012,0.000345256,0.00007391141,0.0001866013],"category_scores_gemma":[0.00001470159,0.000196134,0.00005277929,0.0002865805,0.0002592785,0.0002244565,0.001251573,0.0002984503,0.0002548991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007837237,"about_ca_system_score_gemma":0.000002063674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050741,"about_ca_topic_score_gemma":0.0003311791,"domain_scores_codex":[0.9982252,0.0001555772,0.0002512989,0.0005518358,0.000388149,0.0004280023],"domain_scores_gemma":[0.9994908,0.00005386373,0.00006274314,0.0002904476,5.901543e-7,0.0001015333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002343414,0.0002390549,0.909853,0.000003388199,0.00001103693,0.002191556,0.02714443,0.0006742147,0.05874263,4.972358e-8,0.00006232321,0.001054862],"study_design_scores_gemma":[0.001111775,0.000236454,0.9235463,0.00001869608,0.00003841491,0.0001313933,0.0671569,0.0003718735,0.006412258,0.00000255784,0.0006157219,0.00035765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986111,0.0001197843,0.000001696086,0.0002351658,0.0001132067,0.0006445965,0.00001085912,0.00002083662,0.0002427439],"genre_scores_gemma":[0.9993438,0.00003581697,0.0003880289,0.0001150367,0.00004572897,0.00001544207,0.000001224249,0.00001719526,0.00003780299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05233038,"threshold_uncertainty_score":0.7998113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0571122275354809,"score_gpt":0.230067213898503,"score_spread":0.1729549863630221,"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."}}