{"id":"W3158477740","doi":"10.1111/ddi.13284","title":"Environmental DNA of preservative ethanol performed better than water samples in detecting macroinvertebrate diversity using metabarcoding","year":2021,"lang":"en","type":"article","venue":"Diversity and Distributions","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Environmental DNA; Biodiversity; Benthic zone; Biology; Invertebrate; Ecology; Phylum; Taxonomic rank; Metagenomics; UniFrac; Taxon; 16S ribosomal RNA; Paleontology","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":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0001685075,0.0001615134,0.0002119809,0.00003627071,0.002289872,0.00001417275,0.0001842031,0.00007443118,0.0008261004],"category_scores_gemma":[0.00003233093,0.0001582789,0.00008849933,0.0001350435,0.0005663227,0.0004670638,0.009818576,0.0001574994,0.00003146573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003371147,"about_ca_system_score_gemma":0.000002437134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001509036,"about_ca_topic_score_gemma":0.0002461393,"domain_scores_codex":[0.9987561,0.00009626723,0.000155467,0.0003690103,0.0002891561,0.000333965],"domain_scores_gemma":[0.9996341,0.00005747878,0.00006256226,0.0001543173,0.000005265194,0.00008624481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001541714,0.0001030392,0.9180556,0.00001112111,0.00004254614,0.00002179219,0.003762558,0.00006016668,0.07776888,0.000008346175,0.00002011398,0.0001304524],"study_design_scores_gemma":[0.0004247221,0.00001926616,0.8112049,0.00001182742,0.00007465287,0.00000516825,0.003122665,0.0002487979,0.1844998,0.000179181,0.00004144633,0.0001676238],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985707,0.00003931266,0.0003090646,0.0001244132,0.0000406976,0.0001282328,0.0006149438,0.00001259251,0.0001599821],"genre_scores_gemma":[0.9985108,0.00007041376,0.001229655,0.00005645535,0.000006269672,0.000001163299,0.00008680495,0.000003550781,0.00003487266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1068507,"threshold_uncertainty_score":0.999009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04298182126402165,"score_gpt":0.2177786985147784,"score_spread":0.1747968772507567,"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."}}