{"id":"W4307627826","doi":"10.5281/zenodo.7264066","title":"Optimised DNA isolation from marine sponges for natural sampler DNA (nsDNA) metabarcoding","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Isolation (microbiology); Sponge; DNA; Biology; Ancient DNA; Natural (archaeology); Environmental DNA; DNA extraction; Computational biology; Genetics; Ecology; Microbiology; Polymerase chain reaction; Biodiversity; Paleontology; Gene; Medicine","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":[0.004371824,0.001769735,0.00193911,0.002870906,0.001379872,0.002255405,0.002509183,0.0009137126,0.1102083],"category_scores_gemma":[0.01009476,0.001978923,0.001947608,0.003238531,0.0006379976,0.001358445,0.003146699,0.003062702,0.1281851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007770365,"about_ca_system_score_gemma":0.002097589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002477028,"about_ca_topic_score_gemma":0.008393766,"domain_scores_codex":[0.996549,0.0005932269,0.0003624629,0.0009990257,0.001179424,0.0003168923],"domain_scores_gemma":[0.9964572,0.00119418,0.0001943295,0.0009803419,0.0009354604,0.0002385389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001661442,0.0001442586,0.003905562,0.003743926,0.0002802792,0.0002159396,0.0005449334,0.001237984,0.2148374,0.003025422,0.6794745,0.09092835],"study_design_scores_gemma":[0.0003417444,0.0003701483,0.01322262,0.0004148547,0.0002174813,0.0004424036,0.0001287139,0.00396776,0.1378859,0.002478224,0.8403306,0.0001995053],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.01383896,0.001163473,0.2275839,0.00108224,0.001474616,0.002261759,0.6593474,0.06269027,0.03055742],"genre_scores_gemma":[0.01284164,0.0007252981,0.2770664,0.001223035,0.0001917071,0.00380273,0.6478887,0.02481041,0.03145007],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1102083,"threshold_uncertainty_score":0.3686836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095002250296314,"score_gpt":0.2156666262982101,"score_spread":0.184716603795247,"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."}}