{"id":"W4287958791","doi":"10.1002/edn3.335","title":"Comparative analysis of zooplankton diversity in freshwaters: What can we gain from metagenomic analysis?","year":2022,"lang":"en","type":"article","venue":"Environmental DNA","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Montréal; Concordia University; McGill University; Bureau de Coopération Interuniversitaire","funders":"Fonds de recherche du Québec – Nature et technologies; Groupe de recherche interuniversitaire en limnologie; Concordia University; Ministerio de Economía y Competitividad; Natural Sciences and Engineering Research Council of Canada; Liber Ero Foundation; Université du Québec à Montréal; Canada Research Chairs; McGill University","keywords":"Metagenomics; Biology; Biodiversity; Zooplankton; Environmental DNA; Genetic diversity; Ecology; Computational biology; Archaea; Community structure; Evolutionary biology; Gene; Genetics","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":[0.0002793293,0.0003193505,0.0008335131,0.000439989,0.000597054,0.00002579029,0.0007086982,0.00005751228,0.02306641],"category_scores_gemma":[0.000002420757,0.0003552973,0.0004495287,0.001316614,0.0006302246,0.0003796876,0.004573063,0.0002621838,0.000193689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001812442,"about_ca_system_score_gemma":0.000001939102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00517317,"about_ca_topic_score_gemma":0.003521447,"domain_scores_codex":[0.9971617,0.0003442714,0.0004144197,0.0008053143,0.0008740953,0.0004002335],"domain_scores_gemma":[0.9989678,0.0001194926,0.0002706418,0.0005171813,5.786619e-7,0.000124306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006104099,0.0004049791,0.874364,0.000001271195,0.003044199,0.00002921875,0.01308656,0.09426913,0.01437148,0.000001441295,0.0001792637,0.0001874582],"study_design_scores_gemma":[0.0005147761,0.00009341374,0.96523,0.000002409175,0.003392567,3.846001e-7,0.02284071,0.002918431,0.003952768,0.00004791062,0.0006442231,0.0003624179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964331,0.0004849827,0.00002250976,0.0001221253,0.00008827481,0.0002888931,0.002157595,0.00001695122,0.0003855881],"genre_scores_gemma":[0.9976204,0.0004512018,0.0005238116,0.0001848698,0.000006056068,0.00002324363,0.0009082754,0.000008700579,0.0002734664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0913507,"threshold_uncertainty_score":0.9998899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270313338928139,"score_gpt":0.2157851355949157,"score_spread":0.1930820022056343,"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."}}