{"id":"W2899251903","doi":"10.3897/mbmg.2.28963","title":"Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method","year":2018,"lang":"en","type":"article","venue":"Metabarcoding and Metagenomics","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Fisheries and Oceans Canada","funders":"Natural Environment Research Council; Polar Knowledge Canada; National Science Foundation","keywords":"Environmental DNA; Species richness; Isoamyl alcohol; Extraction (chemistry); Biology; DNA extraction; Biodiversity; Seawater; Filter (signal processing); Chromatography; Ecology; Alcohol; Chemistry; Polymerase chain reaction; Biochemistry; Gene","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":[0.000992342,0.0002243971,0.0003307494,0.00004486421,0.0006061787,0.00004072901,0.0001985348,0.00006198981,0.0001402117],"category_scores_gemma":[0.00008902436,0.0001514713,0.00008934182,0.0001064354,0.0009947892,0.0002304382,0.0008675219,0.0001229141,0.000004240812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001034287,"about_ca_system_score_gemma":0.000002081476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000219604,"about_ca_topic_score_gemma":0.00001175713,"domain_scores_codex":[0.9986771,0.000188419,0.0003137823,0.000351776,0.0002151614,0.0002537013],"domain_scores_gemma":[0.9989796,0.0003602801,0.0003255208,0.0002776339,0.000004228712,0.00005271902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004390117,0.00003264572,0.0139507,0.00003438135,0.0001239419,0.000001289332,0.0005834174,0.00006758071,0.9757054,0.0000100496,0.000004778514,0.009441902],"study_design_scores_gemma":[0.0003224238,0.00009943545,0.1488242,0.00001194671,0.0005863653,0.00004296638,0.0007931745,0.0009968468,0.8471712,0.00005936279,0.0009143216,0.0001776967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948125,0.0003760103,0.003935302,0.00003805134,0.000290596,0.0003764632,0.00002334862,0.00001045211,0.0001372421],"genre_scores_gemma":[0.9684215,0.0006478324,0.03072752,0.00004276312,0.00005663739,0.00000777524,0.000002509025,0.00001689908,0.00007656836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1348735,"threshold_uncertainty_score":0.6176821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164414787445479,"score_gpt":0.2307170889689732,"score_spread":0.2142756102244253,"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."}}