{"id":"W4407018781","doi":"10.70227/kjst9766","title":"Optimization of Methods for the Collection of Larval Sea Lamprey Environmental DNA (eDNA) from Great Lakes Tributaries","year":2025,"lang":"en","type":"article","venue":"Laurentian :","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Fish and Wildlife Service; University of Toledo; Great Lakes Fishery Commission","keywords":"Tributary; Lamprey; Environmental DNA; Larva; Fishery; Environmental science; Ecology; Geography; Biology; Biodiversity; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003275977,0.0008306103,0.000464757,0.001107797,0.0005335392,0.0008161596,0.0008839382,0.0006636831,0.000668194],"category_scores_gemma":[0.003969315,0.0004119323,0.0005934456,0.0007863584,0.0007311385,0.0005035002,0.0008101155,0.0005111268,0.0004808068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006560764,"about_ca_system_score_gemma":0.00144154,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004991919,"about_ca_topic_score_gemma":0.01821477,"domain_scores_codex":[0.9972205,0.0005752294,0.000359046,0.0007194953,0.0009707644,0.0001550302],"domain_scores_gemma":[0.9977254,0.0005926407,0.0004914752,0.0002390638,0.0008627773,0.00008867065],"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":[0.000101264,0.00008935339,0.01318561,0.0003138776,0.00004440855,0.00007077333,0.000255813,0.001094804,0.9654859,0.00008657563,0.0001900231,0.01908152],"study_design_scores_gemma":[0.00004312878,0.001604022,0.1225359,0.000135271,0.0001967151,0.000512967,0.0006279493,0.008806397,0.8532948,0.0002263061,0.01192573,0.00009075464],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.76601,0.002140533,0.223629,0.0003683277,0.00009872291,0.003186831,0.001964792,0.0006340243,0.001967756],"genre_scores_gemma":[0.3828644,0.001837379,0.6050657,0.00039937,0.00004345748,0.003623761,0.004145739,0.0001504526,0.001869771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9950081,"threshold_uncertainty_score":0.01732522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212265425738935,"score_gpt":0.2498349100274381,"score_spread":0.2377122557700488,"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."}}