{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000268946,0.000248604,0.0002211926,0.00006849709,0.0006970342,0.00002508895,0.0003541991,0.00004721688,0.009998521],"category_scores_gemma":[0.00001277711,0.000276244,0.00006105467,0.0002771517,0.0001926943,0.00009909187,0.003398094,0.0001985065,0.0007691568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006902308,"about_ca_system_score_gemma":0.000002828743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003711055,"about_ca_topic_score_gemma":0.00002589155,"domain_scores_codex":[0.9979517,0.00008686778,0.0002311454,0.0007114971,0.0006328479,0.0003858698],"domain_scores_gemma":[0.9992451,0.00004342042,0.0000925337,0.00042843,0.000001528772,0.00018898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002204471,0.0007437,0.7206493,0.00001504001,0.0001069769,0.00003071875,0.003266733,0.004057671,0.06066288,0.00004494571,0.1782187,0.03198284],"study_design_scores_gemma":[0.0007156737,0.0002515852,0.5601894,0.000001709824,0.00003708775,0.00001688298,0.0005382478,0.002501632,0.0004318322,0.00006749838,0.4348523,0.0003961172],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932681,0.00006679174,0.0002502046,0.001490108,0.0001227549,0.0008762623,0.000173737,0.0002680468,0.003483944],"genre_scores_gemma":[0.9693563,0.0002467707,0.0207642,0.002197809,0.00004215791,0.0004214976,0.0002642548,0.00005029086,0.006656724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2566335,"threshold_uncertainty_score":0.9999689,"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."}}