{"id":"W3186884848","doi":"10.3897/mbmg.5.68938","title":"The power of metabarcoding: Can we improve bioassessment and biodiversity surveys of stream macroinvertebrate communities?","year":2021,"lang":"en","type":"article","venue":"Metabarcoding and Metagenomics","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Canada First Research Excellence Fund; University of Guelph","keywords":"Biodiversity; Biology; Ecology; Taxon; Taxonomic rank; Environmental DNA; Chironomidae; DNA barcoding; Benthic zone; Identification (biology); Larva","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03654096,0.002687325,0.002591227,0.004812661,0.0007341554,0.00401128,0.002797605,0.003240978,0.001351382],"category_scores_gemma":[0.07780591,0.001018785,0.001726807,0.005427002,0.001599847,0.009456645,0.002007728,0.002034502,0.001662974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006711438,"about_ca_system_score_gemma":0.001270912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003190855,"about_ca_topic_score_gemma":0.005411918,"domain_scores_codex":[0.9867997,0.007754879,0.0007854411,0.002540529,0.001691099,0.000428396],"domain_scores_gemma":[0.9409351,0.03750928,0.007001276,0.005801595,0.007588624,0.001164071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001066569,0.0002097062,0.3437312,0.002408889,0.001788441,0.0003221414,0.00197498,0.006543389,0.04963379,0.003354985,0.005678455,0.5832875],"study_design_scores_gemma":[0.0002678815,0.003348419,0.6036869,0.003427964,0.003359629,0.003560414,0.003299402,0.1812904,0.0684584,0.04460969,0.08358369,0.001107213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4312444,0.03107309,0.5052827,0.01428642,0.001126033,0.0004704439,0.004392568,0.003924917,0.0081994],"genre_scores_gemma":[0.4032865,0.006777617,0.5822903,0.002685596,0.0004924195,0.0003131923,0.002475088,0.0003831545,0.001296068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03654096,"threshold_uncertainty_score":0.1932493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02338577835131121,"score_gpt":0.2184975225325859,"score_spread":0.1951117441812747,"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."}}