{"id":"W4285095212","doi":"10.1101/2022.07.11.499619","title":"Optimised DNA isolation from marine sponges for natural sampler DNA (nsDNA) metabarcoding","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"CHIST-ERA; Sight Research UK; Ministerio de Ciencia e Innovación; Natural Environment Research Council; Agencia Nacional de Investigación y Desarrollo; Agenția Națională pentru Cercetare și Dezvoltare","keywords":"Sponge; Environmental DNA; DNA extraction; Biology; Biodiversity; DNA; Extraction (chemistry); Ecology; Polymerase chain reaction; Chromatography; Chemistry; Botany; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003643287,0.0006934158,0.0005996696,0.0007879786,0.000442995,0.001123704,0.0007716362,0.0008062107,0.001805261],"category_scores_gemma":[0.004532567,0.0005580415,0.0006761675,0.0005455992,0.0005980777,0.0005740703,0.0009764308,0.0008368025,0.001952313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003507155,"about_ca_system_score_gemma":0.0008866818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001237824,"about_ca_topic_score_gemma":0.003620435,"domain_scores_codex":[0.9974777,0.0006925679,0.0002810502,0.0006532742,0.0007064731,0.0001888947],"domain_scores_gemma":[0.997973,0.000683262,0.0003444873,0.0001999387,0.0006666382,0.0001325316],"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":[0.0001705367,0.00004604793,0.00189085,0.0004073534,0.00002720714,0.00007466708,0.0001490079,0.0008296132,0.9869676,0.0001192272,0.0003541891,0.008963716],"study_design_scores_gemma":[0.00004392431,0.0007306148,0.01295767,0.0001441335,0.00008861943,0.0004238734,0.0001662385,0.007628189,0.9639543,0.0002330978,0.01355481,0.00007451887],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5868385,0.001957712,0.3942629,0.0007825176,0.0002978806,0.001860324,0.006168575,0.004119186,0.003712353],"genre_scores_gemma":[0.3215987,0.001433035,0.6591445,0.0005662251,0.00006782536,0.001727595,0.0104929,0.0007152901,0.004253943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003643287,"threshold_uncertainty_score":0.0192678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01833062207143285,"score_gpt":0.2106885823930441,"score_spread":0.1923579603216112,"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."}}