{"id":"W4313028256","doi":"10.1109/oceans47191.2022.9977218","title":"A Miniaturized and Automated eDNA Sampler: Application to a Marine Environment","year":2022,"lang":"en","type":"article","venue":"OCEANS 2022, Hampton Roads","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Canada First Research Excellence Fund; Mitacs; National Research Council","keywords":"Environmental DNA; Sample (material); Protocol (science); Aquatic environment; Biodiversity; Computer science; Environmental science; Sampling (signal processing); Filter (signal processing); Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001064406,0.0005737946,0.0004929676,0.0007321889,0.0007058542,0.0007186866,0.001060987,0.0008175638,0.002254003],"category_scores_gemma":[0.001032721,0.0004161833,0.0003536499,0.0007466915,0.0005979785,0.0003670823,0.001168145,0.0004904722,0.001052313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076709,"about_ca_system_score_gemma":0.002555178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03234302,"about_ca_topic_score_gemma":0.07447241,"domain_scores_codex":[0.9991758,0.00008882581,0.00003960076,0.0002393691,0.0003887826,0.00006758452],"domain_scores_gemma":[0.9994574,0.0001347379,0.00005628358,0.00006632484,0.0001968372,0.00008848259],"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":[0.0002604074,0.0001462739,0.01784164,0.0004095892,0.00004498044,0.0003759258,0.0004675143,0.001599934,0.8684946,0.0005141311,0.001532751,0.1083123],"study_design_scores_gemma":[0.0001859481,0.001513032,0.09471003,0.0002584513,0.0002041364,0.002935176,0.000993329,0.03768364,0.7573628,0.001030065,0.1028493,0.0002740182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3693472,0.001974056,0.6032551,0.0006742156,0.0003046401,0.003525766,0.005934104,0.006126701,0.008858171],"genre_scores_gemma":[0.2154184,0.001425007,0.7695735,0.000407305,0.00004349056,0.001161837,0.002151385,0.0002030524,0.009615966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03234302,"threshold_uncertainty_score":0.06430954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00508888310828593,"score_gpt":0.1940856527813745,"score_spread":0.1889967696730886,"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."}}