{"id":"W4378464597","doi":"10.1093/icesjms/fsad083","title":"eDNA metabarcoding enriches traditional trawl survey data for monitoring biodiversity in the marine environment","year":2023,"lang":"en","type":"article","venue":"ICES Journal of Marine Science","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Guelph; Bedford Institute of Oceanography; Fisheries and Oceans Canada","funders":"Fisheries and Oceans Canada","keywords":"Environmental DNA; Trawling; Biodiversity; Benthic zone; Invertebrate; Ecology; Biology; Fishery; Benthos; Species richness; Fishing","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":[0.005866505,0.0001502782,0.0001971798,0.0001694821,0.0005687082,0.00008368217,0.002460183,0.0000279115,0.0002657111],"category_scores_gemma":[0.0003123696,0.0001123089,0.00006671428,0.0007495868,0.0009746504,0.001214653,0.003210105,0.0002022898,0.0001138167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002696515,"about_ca_system_score_gemma":0.00001339391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004090581,"about_ca_topic_score_gemma":0.00007356729,"domain_scores_codex":[0.9976088,0.0001106314,0.000319861,0.0003868087,0.00118499,0.0003888403],"domain_scores_gemma":[0.9986496,0.0006034618,0.0002525068,0.0003876296,0.00001085885,0.00009596378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003162707,0.00009908537,0.989647,0.000005463181,0.00001464459,0.00002076094,0.0004284293,0.0006789031,0.001917988,0.000003838857,0.001548861,0.005603394],"study_design_scores_gemma":[0.0003678268,0.0001038314,0.9958061,0.000006025026,0.00002688873,0.00001773092,0.0009976813,0.0002422434,0.0005531834,0.0002432691,0.001506713,0.0001284956],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979469,0.00002669059,0.0000488177,0.0007494467,0.0003229597,0.00018965,0.0001461802,0.000008821544,0.0005605094],"genre_scores_gemma":[0.9898748,0.0005765056,0.009284447,0.00007033251,0.00008385651,0.000003333456,0.00003738538,0.000004158506,0.00006513373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00923563,"threshold_uncertainty_score":0.4579825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1440887429367803,"score_gpt":0.2818238727743015,"score_spread":0.1377351298375212,"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."}}