{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001915943,0.0001337692,0.0002050015,0.00003208909,0.0002748642,0.00001155881,0.0002423778,0.00007516256,0.001495171],"category_scores_gemma":[0.00004284565,0.0001100809,0.0001233746,0.0001649842,0.0005331265,0.0001138361,0.0002701042,0.00006208279,0.000008398573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001261057,"about_ca_system_score_gemma":0.000003512742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003486023,"about_ca_topic_score_gemma":0.0002523274,"domain_scores_codex":[0.9991234,0.00009562085,0.0002232828,0.0002466524,0.0001578906,0.000153134],"domain_scores_gemma":[0.9993078,0.0002964448,0.0001346829,0.0002337323,0.000003272321,0.00002406652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002236786,0.000243455,0.9242446,0.00003249838,0.000379567,2.94844e-7,0.001309654,0.01662477,0.03454496,0.00004055622,0.01349182,0.008864082],"study_design_scores_gemma":[0.001706484,0.0002043897,0.737314,0.00003985436,0.0006727509,6.946337e-7,0.001719301,0.01458672,0.2048872,0.000649033,0.03791318,0.0003063864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7351153,0.001828578,0.255605,0.001025182,0.0009689373,0.001849416,0.001524065,0.0000634747,0.002020019],"genre_scores_gemma":[0.8219389,0.0007424312,0.1736743,0.0001413917,0.00003261655,0.00007666089,0.0003724255,0.00001917702,0.003002075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1869307,"threshold_uncertainty_score":0.9994176,"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."}}