{"id":"W2980835560","doi":"10.1002/edn3.35","title":"Comparing eDNA metabarcoding and species collection for documenting Arctic metazoan biodiversity","year":2019,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski; Fisheries and Oceans Canada; Université Laval","funders":"Fisheries and Oceans Canada; Churchill Northern Studies Centre; ArcticNet","keywords":"Environmental DNA; Biodiversity; Arctic; Ecology; Phylum; Species richness; Biology; Invertebrate; Beta diversity; Marine biodiversity; DNA barcoding; Pelagic zone; Fishery","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":[0.002852565,0.0006239601,0.0003695671,0.002860583,0.001036635,0.001373657,0.0005711812,0.0004140549,0.0006641134],"category_scores_gemma":[0.004344394,0.0003064823,0.0003242321,0.002346131,0.0005310964,0.0004224346,0.0009624669,0.0002693736,0.0002201271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001270711,"about_ca_system_score_gemma":0.001956044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.078765,"about_ca_topic_score_gemma":0.3094805,"domain_scores_codex":[0.9977462,0.0005288072,0.0002031659,0.0005534914,0.0007526273,0.0002157313],"domain_scores_gemma":[0.9962155,0.0008366017,0.000845088,0.0002385317,0.001647345,0.0002169645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005829723,0.00008123269,0.7204256,0.0004234315,0.0003835975,0.0001170464,0.001341517,0.001006307,0.2077489,0.0002462307,0.0003412514,0.06730175],"study_design_scores_gemma":[0.00001249719,0.0002252155,0.9173297,0.0000936881,0.0002617603,0.0002835375,0.001587113,0.003443434,0.07199149,0.00009597646,0.004633669,0.00004185798],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687603,0.001687069,0.02338253,0.0001329927,0.00003768485,0.0001767832,0.002056386,0.0001532802,0.003612948],"genre_scores_gemma":[0.9023552,0.001091493,0.09177253,0.0001964499,0.00001924917,0.0001660366,0.00262639,0.00005334034,0.00171925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.078765,"threshold_uncertainty_score":0.1566131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172696622891152,"score_gpt":0.1903587254623572,"score_spread":0.173089063173242,"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."}}