{"id":"W4405497520","doi":"10.1002/edn3.70015","title":"Evaluating Sampling Designs to Survey Fish Diversity in Lakes From Northern Temperate Zones","year":2024,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Génome Québec","keywords":"Temperate climate; Fish <Actinopterygii>; Sampling (signal processing); Diversity (politics); Fishery; Geography; Ecology; Sampling design; Environmental science; Biology; Engineering; Population","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":["insufficient_payload"],"category_scores_codex":[0.0006419509,0.0003503225,0.0002727328,0.00007679438,0.0005278017,0.00008209009,0.0004654696,0.000104431,0.003509613],"category_scores_gemma":[0.00005251892,0.0003668101,0.00009605208,0.00026666,0.0002930702,0.000388182,0.002505498,0.0002514532,0.006082466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213757,"about_ca_system_score_gemma":0.0000033505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004077898,"about_ca_topic_score_gemma":0.006808099,"domain_scores_codex":[0.997288,0.000226171,0.0003059816,0.0009580402,0.0007068089,0.0005149718],"domain_scores_gemma":[0.9991529,0.0002799374,0.00004734627,0.0003344013,9.672476e-7,0.0001844763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003546274,0.0001162381,0.9570577,0.000004954103,0.00003746806,0.00003214009,0.002748204,0.002828431,0.03287498,6.515396e-7,0.0006595153,0.003604251],"study_design_scores_gemma":[0.0002746237,0.0001162796,0.9949526,0.00004224374,0.00002910227,0.000001750858,0.0007243842,0.0004224027,0.002063646,0.0001049247,0.0008266296,0.0004413768],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975249,0.000162262,0.0001584687,0.0001806968,0.0002843112,0.0004416913,0.0008453357,0.00008837774,0.0003140014],"genre_scores_gemma":[0.9948754,0.00008430045,0.003815731,0.0004067156,0.00004498546,0.00002194582,0.0001995078,0.00003363167,0.0005177481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03789493,"threshold_uncertainty_score":0.9998784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1031422426852203,"score_gpt":0.2872249845323366,"score_spread":0.1840827418471163,"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."}}