{"id":"W2805617713","doi":"10.1038/s41598-018-27048-2","title":"Effects of sampling effort on biodiversity patterns estimated from environmental DNA metabarcoding surveys","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Université Laval","funders":"National Science Foundation","keywords":"Environmental DNA; Species richness; Biodiversity; Sampling (signal processing); Taxon; Phylum; Biology; Ecology; Taxonomic rank; Sampling design; Habitat; Global biodiversity; Computer 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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001070122,0.0002728072,0.0003074215,0.00009276044,0.0007991597,0.0000661176,0.0003190168,0.00008575962,0.002041188],"category_scores_gemma":[0.00007738712,0.0002615362,0.0001431442,0.0002353336,0.001659948,0.0002713242,0.0008999201,0.0001188201,0.001308275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002998782,"about_ca_system_score_gemma":0.000003805777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007452682,"about_ca_topic_score_gemma":0.0000485713,"domain_scores_codex":[0.997005,0.0001171126,0.0003890515,0.001104602,0.0009540592,0.0004301326],"domain_scores_gemma":[0.9986623,0.0001310231,0.0003507541,0.000689785,0.000006198689,0.0001599615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008152085,0.0001663207,0.7873717,0.000008766887,0.00004139372,0.00008454054,0.0004209121,0.00003473929,0.2095259,3.127652e-7,0.001303787,0.001033533],"study_design_scores_gemma":[0.0001460817,0.00006886654,0.6432781,0.0000277353,0.00005065572,0.00000479038,0.00007272481,0.00001934394,0.3554616,0.00009365611,0.0005948538,0.0001816236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962949,0.0000314013,0.0002482611,0.00001807098,0.002480222,0.0003757974,0.00009926436,0.000060643,0.0003914056],"genre_scores_gemma":[0.9974908,0.00001229928,0.002078054,0.00004263,0.00003000142,0.000004874403,0.0001310196,0.00001022517,0.0002000444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1459357,"threshold_uncertainty_score":0.9999837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413364288695657,"score_gpt":0.2288592236637385,"score_spread":0.204725580776782,"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."}}